CN113487100B - Global accurate prediction method and system for photovoltaic power generation output - Google Patents
Global accurate prediction method and system for photovoltaic power generation output Download PDFInfo
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Abstract
The invention discloses a global accurate prediction method and a global accurate prediction system for the output of photovoltaic power generation. The invention adopts the technical scheme that: carrying out numerical accumulation on the single power prediction result of each photovoltaic power station in the regional power grid to obtain a global prediction value of the photovoltaic power generation output of the regional power grid; generating a coordinate distribution diagram of a regional power grid photovoltaic power station; calculating the time-space correlation between the output of each photovoltaic power station in the regional power grid according to the difference between the predicted output and the actual output of each photovoltaic power station in the regional power grid, and establishing a predicted error distribution timing diagram; calculating the front and back positions of the photovoltaic output error movement, and estimating a prediction error of the next moment; and calculating the correlation coefficient of the photovoltaic power station prediction error by combining the geographic coordinate information and the meteorological information of the area where each photovoltaic power station is located, and correcting the prediction error at the next moment. According to the invention, global accurate prediction of the photovoltaic power generation output is realized on the basis of fusion of geographic weather information, and the photovoltaic absorption space and the operation stability of a power grid are improved.
Description
Technical Field
The invention relates to the field of global accurate prediction of photovoltaic power generation output, in particular to a global accurate prediction method and a global accurate prediction system of photovoltaic power generation output by fusing geographic meteorological information.
Background
The photovoltaic power generation output power is influenced by meteorological factors such as solar radiation, cloud cover and the like, has strong intermittence and randomness, so that the improvement of the overall prediction accuracy of the photovoltaic output has great significance for the overall safe and stable and economic operation of the regional power system.
At present, research on photovoltaic output prediction at home and abroad is mainly focused on prediction of a single power station through a physical modeling or statistical method, and related results are very mature, but related research on regional photovoltaic power prediction is very few. Large-scale photovoltaic power stations and distributed photovoltaic grid connection bring certain difficulties to power dispatching and peak shaving of the whole regional power system. Therefore, global photovoltaic power prediction research is more important.
In summary, how to provide a global accurate prediction method for photovoltaic power generation output, based on the implementation of global photovoltaic output prediction, to integrate geographical meteorological information of a regional power grid, and to correct and reduce prediction errors of the regional power grid on photovoltaic output is a problem to be solved by those skilled in the art.
Disclosure of Invention
In view of the above, the invention aims to provide a global accurate prediction method and a system for generating power by photovoltaic power fused with geographical weather information by making up for the defects in the prior art and system, and the global accurate prediction for generating power by photovoltaic power is realized on the basis of fusing the geographical weather information so as to improve the photovoltaic absorption space and the operation stability of a power grid.
In order to achieve the above purpose, the present invention provides the following technical solutions: the global accurate prediction method for the photovoltaic power generation output comprises the following steps:
step 1), carrying out numerical accumulation on single power prediction results of each photovoltaic power station in the regional power grid to obtain a global prediction value of the photovoltaic power generation output of the regional power grid;
step 2), acquiring longitude and latitude coordinate information of each photovoltaic power station in the regional power grid, and generating a coordinate distribution diagram of the photovoltaic power stations of the regional power grid;
step 3), weather information of each photovoltaic power station in the regional power grid is obtained;
step 4), calculating the difference between the predicted output and the actual output of each photovoltaic power station in the regional power grid;
step 5), calculating the time-space correlation between the output of the photovoltaic power stations in the regional power grid according to the difference between the predicted output and the actual output of each photovoltaic power station in the regional power grid, and establishing a predicted error distribution timing diagram;
step 6), marking the corresponding positions of the photovoltaic power stations in the coordinate distribution diagram after the standardization processing of the prediction errors of the photovoltaic power stations;
step 7), calculating the front and back positions of the photovoltaic output error movement according to the correlation of the error time sequence, namely calculating the source direction and distance of the error, and estimating the prediction error of the next moment;
step 8), calculating the correlation coefficient of the photovoltaic power station prediction error by combining the geographic coordinate information and the meteorological information of the area where each photovoltaic power station is located;
step 9), correcting the prediction error of the next moment in the step 7) according to the calculated correlation coefficient.
Further, the meteorological information in step 3) includes solar irradiance, wind speed, wind direction, cloud size, and cloud wind direction.
Further, in step 6), the prediction error P err The formula of (2) is as follows:
P err ＝(P pre -P real )/P N (1)
wherein P is pre To predict power, P rea1 To actually measure power, P N Is the rated power of the photovoltaic power station.
Further, in step 7), the prediction error at the next time is:
P′ err (x,y,t+Δt)＝P err (x-v x Δt,y-v y Δt,t) (2)
wherein P is err (x, y, t) represents the prediction error, v, at the (x, y) position in the error profile at time t x And v y Representing the components of wind speed in the x and y directions, respectively.
Further, in step 8), the correlation coefficient of the photovoltaic power plant prediction error is as follows:
wherein S1 (t) is a correction error at the time of weak correlation, and the correction error is kept unchanged S1 (t) =p err (x, y, t), wherein x, y is the position of the plant to be corrected;
s2 (t) is a correction error in the case of strong correlation, and at this time, the current error based on the position of the highest point of correlation of the error time series is regarded as the correction error, S2 (t) =p err (x 1 ,y 1 ,t-Δt)，x 1 ，y 1 The position of the highest point of the error time sequence correlation;
when the correlation coefficient is between the weak correlation and the strong correlation, the correction is performed by a linear fusion mode:
α＝-2.5ρ+2 (4)
alpha is a variable decreasing from 1 to 0, and when ρ <0.4, then the correction error is calculated by S1; along with the improvement of the correlation coefficient, the error movement mode is gradually clear, and the weight of the S2 component is improved; when ρ >0.8, the correction error is calculated entirely using S2.
Further, in step 9), the correction value of the prediction error at the next time is as follows:
P′ pre (t+Δt)＝P pre (t+Δt)+P′ err (t+Δt)·P N (5)。
the invention adopts another technical scheme that: photovoltaic power generation output global accurate prediction system, it includes:
the photovoltaic power generation output global prediction unit is used for carrying out numerical accumulation on single power prediction results of each photovoltaic power station in the regional power grid to obtain a regional power grid photovoltaic power generation output global prediction value;
the coordinate distribution map generation unit is used for obtaining longitude and latitude coordinate information of each photovoltaic power station in the regional power grid and generating a coordinate distribution map of the photovoltaic power station of the regional power grid;
the meteorological information acquisition unit is used for acquiring meteorological information of each photovoltaic power station in the regional power grid;
the predicted output and actual output difference calculation unit is used for calculating the difference between the predicted output and the actual output of each photovoltaic power station in the regional power grid;
the prediction error distribution time sequence diagram establishing unit calculates the time-space correlation between the output of the photovoltaic power stations in the regional power grid according to the difference between the predicted output and the actual output of each photovoltaic power station in the regional power grid, and establishes a prediction error distribution time sequence diagram;
the prediction error coordinate marking unit is used for marking the corresponding positions of the photovoltaic power stations in the coordinate distribution diagram after the prediction error of the photovoltaic power stations is standardized;
the prediction error estimation unit at the next moment calculates the front and back positions of the photovoltaic output error movement according to the correlation of the error time sequence, namely calculates the source direction and distance of the error, and estimates the prediction error at the next moment;
the prediction error correlation coefficient calculation unit is used for calculating the correlation coefficient of the prediction error of the photovoltaic power station by combining the geographic coordinate information and the meteorological information of the area where each photovoltaic power station is located;
and a prediction error correction unit for correcting the prediction error of the next moment according to the calculated correlation coefficient.
The invention has the following beneficial effects: the method estimates the future prediction error of the photovoltaic single station, thereby realizing the advanced correction of the predicted power of the photovoltaic power station. Particularly, at the moment of greatly reducing the output of the photovoltaic power station caused by cloud layer moving shielding, the method can enable the predicted power to have better tracking and accuracy by correcting the predicted power of the photovoltaic power station in advance by utilizing the interactive influence of the output data of the regional multi-photovoltaic power station. The method can simply, accurately and effectively realize overall accurate prediction of the photovoltaic power generation output on the basis of fusion of geographic weather information, and improves the photovoltaic absorption space and the operation stability of the power grid.
Drawings
FIG. 1 is a flowchart of a global accurate prediction method for photovoltaic power generation output according to embodiment 1 of the present invention;
fig. 2 is a coordinate distribution diagram of a regional grid photovoltaic power station in embodiment 1 of the present invention;
fig. 3 is a block diagram of the overall precise prediction system for photovoltaic power generation output according to embodiment 2 of the present invention.
Detailed Description
The invention is described in further detail below with reference to the drawings.
Example 1
The embodiment provides a global accurate prediction method for photovoltaic power generation output, which comprises the following steps as shown in fig. 1:
1) And carrying out numerical accumulation on the single power prediction result of each photovoltaic power station in the regional power grid to obtain a global prediction value of the photovoltaic power generation output of the regional power grid.
2) And acquiring longitude and latitude coordinate information of each photovoltaic power station in the regional power grid, and generating a coordinate distribution diagram of the photovoltaic power stations of the regional power grid, as shown in fig. 2.
3) And acquiring meteorological information of each photovoltaic power station in the regional power grid, such as solar irradiance, wind speed, cloud layer size, cloud layer wind direction and the like.
4) And calculating the difference between the predicted output and the actual output of each photovoltaic power station in the regional power grid.
5) And calculating the time-space correlation between the output of the photovoltaic power stations in the regional power grid according to the difference between the predicted output and the actual output of each photovoltaic power station in the regional power grid, and establishing a prediction error distribution timing diagram.
6) After the prediction error of the photovoltaic power stations is standardized, marking the prediction error on the corresponding position of each photovoltaic power station in the coordinate distribution diagram; prediction error P err The calculation formula is as follows:
P err ＝(P pre -P real )/P N (1)
wherein P is pre To predict power, P real To actually measure power, P N Is the rated power of the power station.
7) Searching and matching the front and rear positions of the movement of the photovoltaic output error according to the correlation of the error time sequence, calculating the source direction and distance of the error, and estimating the prediction error at the next time;
prediction error at the next moment:
P′ err (x,y,t+Δt)＝P err (x-v x Δt,y-v y Δt,t) (2)
wherein P is err (x, y, t) represents the error value, v, at the (x, y) position in the error profile at time t x And v y Representing the components of wind speed in the x and y directions, respectively.
8) Calculating the correlation coefficient of the photovoltaic power station prediction error by combining the geographic coordinate information and the meteorological information of the area where each photovoltaic power station is located, such as the wind direction and wind velocity measurement data of cloud layers;
wherein S1 (t) is a correction error at the time of weak correlation, and the correction error is kept unchanged S1 (t) =p err (x, y, t), where x,y is the position of the power station to be corrected;
s2 (t) is a correction error in the case of strong correlation, and at this time, S2 (t) =p, the error at this time is taken as a correction error based on the place of origin of the error err (x 1 ,y 1 ,t-Δt)，x 1 ，y 1 Is the position of the highest point of the error time series correlation.
When the correlation coefficient is between the weak correlation and the strong correlation, the correction is performed by a linear fusion mode:
α＝-2.5ρ+2 (4)
alpha is a variable which decreases from 1 to 0, and when ρ is less than or equal to 0.4, a correction error is calculated through S1; along with the improvement of the correlation coefficient, the error movement mode is gradually clear, and the weight of the S2 component is improved; when rho is more than or equal to 0.8, the correction error is calculated by completely adopting an S2 method.
9) Correcting the error calculated in the step (5) according to the calculated correlation coefficient;
correction value of predicted value error at next time:
P′ pre (t+Δt)＝P pre (t+Δt)+P′ err (t+Δt)·P N (5)。
example 2
The embodiment provides a global accurate prediction system for photovoltaic power generation output, as shown in fig. 3, which includes:
the photovoltaic power generation output global prediction unit is used for carrying out numerical accumulation on single power prediction results of each photovoltaic power station in the regional power grid to obtain a regional power grid photovoltaic power generation output global prediction value;
the coordinate distribution map generation unit is used for obtaining longitude and latitude coordinate information of each photovoltaic power station in the regional power grid and generating a coordinate distribution map of the photovoltaic power station of the regional power grid;
the meteorological information acquisition unit is used for acquiring meteorological information of each photovoltaic power station in the regional power grid;
the predicted output and actual output difference calculation unit is used for calculating the difference between the predicted output and the actual output of each photovoltaic power station in the regional power grid;
the prediction error distribution time sequence diagram establishing unit calculates the time-space correlation between the output of the photovoltaic power stations in the regional power grid according to the difference between the predicted output and the actual output of each photovoltaic power station in the regional power grid, and establishes a prediction error distribution time sequence diagram;
the prediction error coordinate marking unit is used for marking the corresponding positions of the photovoltaic power stations in the coordinate distribution diagram after the prediction error of the photovoltaic power stations is standardized;
the prediction error estimation unit at the next moment calculates the front and back positions of the photovoltaic output error movement according to the correlation of the error time sequence, namely calculates the source direction and distance of the error, and estimates the prediction error at the next moment;
the prediction error correlation coefficient calculation unit is used for calculating the correlation coefficient of the prediction error of the photovoltaic power station by combining the geographic coordinate information and the meteorological information of the area where each photovoltaic power station is located;
and a prediction error correction unit for correcting the prediction error of the next moment according to the calculated correlation coefficient.
In the prediction error coordinate marking unit, the prediction error P err The formula of (2) is as follows:
P err ＝(P pre -P real )/P N (1)
wherein P is pre To predict power, P real To actually measure power, P N Is the rated power of the photovoltaic power station.
In the prediction error estimation unit at the next time, the prediction error at the next time is:
P′ err (x,y,t+Δt)＝P err (x-v x Δt,y-v y Δt,t) (2)
wherein P is err (x, y, t) represents the prediction error, v, at the (x, y) position in the error profile at time t x And v y Representing the components of wind speed in the x and y directions, respectively.
In the prediction error correlation coefficient calculation unit, the correlation coefficient of the photovoltaic power station prediction error is as follows:
wherein S1 (t) is a correction error at the time of weak correlation, and the correction error is kept unchanged S1 (t) =p err (x, y, t), wherein x, y is the position of the plant to be corrected;
s2 (t) is a correction error in the case of strong correlation, and at this time, the current error based on the position of the highest point of correlation of the error time series is regarded as the correction error, S2 (t) =p err (x 1 ,y 1 ,t-Δt)，x 1 ，y 1 The position of the highest point of the error time sequence correlation;
when the correlation coefficient is between the weak correlation and the strong correlation, the correction is performed by a linear fusion mode:
α＝-2.5ρ+2 (4)
alpha is a variable which decreases from 1 to 0, and when ρ is less than or equal to 0.4, a correction error is calculated through S1; along with the improvement of the correlation coefficient, the error movement mode is gradually clear, and the weight of the S2 component is improved; when rho is more than or equal to 0.8, S2 is completely adopted to calculate correction errors.
In the prediction error correction unit at the next time, the correction value of the prediction error at the next time is as follows:
P′ pre (t+Δt)＝P pre (t+Δt)+P′ err (t+Δt)·P N (5)。
the specific embodiments described herein are intended to be illustrative of only some, but not all, of the embodiments of the invention and other embodiments are within the scope of the invention as would be apparent to one of ordinary skill in the art without undue burden.
Claims (9)
1. The global accurate prediction method for the photovoltaic power generation output is characterized by comprising the following steps:
step 1), carrying out numerical accumulation on single power prediction results of each photovoltaic power station in the regional power grid to obtain a global prediction value of the photovoltaic power generation output of the regional power grid;
step 2), acquiring longitude and latitude coordinate information of each photovoltaic power station in the regional power grid, and generating a coordinate distribution diagram of the photovoltaic power stations of the regional power grid;
step 3), weather information of each photovoltaic power station in the regional power grid is obtained;
step 4), calculating the difference between the predicted output and the actual output of each photovoltaic power station in the regional power grid;
step 5), calculating the time-space correlation between the output of the photovoltaic power stations in the regional power grid according to the difference between the predicted output and the actual output of each photovoltaic power station in the regional power grid, and establishing a predicted error distribution timing diagram;
step 6), marking the corresponding positions of the photovoltaic power stations in the coordinate distribution diagram after the standardization processing of the prediction errors of the photovoltaic power stations;
step 7), calculating the front and back positions of the photovoltaic output error movement according to the correlation of the error time sequence, namely calculating the source direction and distance of the error, and estimating the prediction error of the next moment;
step 8), calculating the correlation coefficient of the photovoltaic power station prediction error by combining the geographic coordinate information and the meteorological information of the area where each photovoltaic power station is located;
step 9), correcting the prediction error of the next moment in the step 7) according to the calculated correlation coefficient;
in step 8), the correlation coefficient of the photovoltaic power plant prediction error is as follows:
wherein P is e ′ rr (t+Δt) is the prediction error at the next time after correction, S1 (t) is the correction error at the time of weak correlation, where the correction error remains unchanged S1 (t) =p, with a low correlation coefficient err (x, y, t), wherein x, y is the position of the plant to be corrected; p (P) err (x, y, t) represents the prediction error at the (x, y) position in the error profile at time t;
s2 (t) is a correction error in the case of strong correlation, and at this time, the current error based on the position of the point of highest correlation of the error time series is regarded as the correction error, S2 (t) =P err (x 1 ,y 1 ,t-Δt)，x 1 ，y 1 The position of the highest point of the error time sequence correlation;
when the correlation coefficient is between the weak correlation and the strong correlation, the correction is performed by a linear fusion mode:
α＝-2.5ρ+2 (4)
alpha is a variable which decreases from 1 to 0, and when ρ is less than or equal to 0.4, a correction error is calculated through S1; along with the improvement of the correlation coefficient, the error movement mode is gradually clear, and the weight of the S2 component is improved; when rho is more than or equal to 0.8, S2 is completely adopted to calculate correction errors.
2. The method of claim 1, wherein the meteorological information in step 3) includes solar irradiance, wind speed, wind direction, cloud size, and cloud wind direction.
3. The global accurate prediction method of photovoltaic power generation output according to claim 1, wherein in step 6), the prediction error P is err The formula of (2) is as follows:
P err ＝(P pre -P real )/P N (1)
wherein P is pre To predict power, P real To actually measure power, P N Is the rated power of the photovoltaic power station.
4. The global accurate prediction method of photovoltaic power generation output according to claim 3, wherein in step 7), the prediction error at the next moment is:
P′ err (x,y,t+Δt)＝P err (x-v x Δt,y-v y Δt,t) (2)
in the formula, v x And v y Representing the components of wind speed in the x and y directions, respectively.
5. The global accurate prediction method of photovoltaic power generation output according to claim 3, wherein in step 9), the correction value of the prediction error at the next time is as follows:
P′ pre (t+Δt)＝P pre (t+Δt)+P′ err (t+Δt)·P N (5)。
6. photovoltaic power generation output global accurate prediction system, its characterized in that includes:
the photovoltaic power generation output global prediction unit is used for carrying out numerical accumulation on single power prediction results of each photovoltaic power station in the regional power grid to obtain a regional power grid photovoltaic power generation output global prediction value;
the coordinate distribution map generation unit is used for obtaining longitude and latitude coordinate information of each photovoltaic power station in the regional power grid and generating a coordinate distribution map of the photovoltaic power station of the regional power grid;
the meteorological information acquisition unit is used for acquiring meteorological information of each photovoltaic power station in the regional power grid;
the predicted output and actual output difference calculation unit is used for calculating the difference between the predicted output and the actual output of each photovoltaic power station in the regional power grid;
the prediction error distribution time sequence diagram establishing unit calculates the time-space correlation between the output of the photovoltaic power stations in the regional power grid according to the difference between the predicted output and the actual output of each photovoltaic power station in the regional power grid, and establishes a prediction error distribution time sequence diagram;
the prediction error coordinate marking unit is used for marking the corresponding positions of the photovoltaic power stations in the coordinate distribution diagram after the prediction error of the photovoltaic power stations is standardized;
the prediction error estimation unit at the next moment calculates the front and back positions of the photovoltaic output error movement according to the correlation of the error time sequence, namely calculates the source direction and distance of the error, and estimates the prediction error at the next moment;
the prediction error correlation coefficient calculation unit is used for calculating the correlation coefficient of the prediction error of the photovoltaic power station by combining the geographic coordinate information and the meteorological information of the area where each photovoltaic power station is located;
a prediction error correction unit for correcting the prediction error of the next time according to the calculated correlation coefficient;
in the prediction error correlation coefficient calculation unit, the correlation coefficient of the prediction error of the photovoltaic power station is as follows:
wherein P' err (t+Δt) is the prediction error at the next time after correction, S1 (t) is the correction error at the time of weak correlation, where the correction error remains unchanged S1 (t) =p, with a low correlation coefficient err (x, y, t), wherein x, y is the position of the plant to be corrected; p (P) err (x, y, t) represents the prediction error at the (x, y) position in the error profile at time t;
s2 (t) is a correction error in the case of strong correlation, and at this time, the current error based on the position of the highest point of correlation of the error time series is regarded as the correction error, S2 (t) =p err (x 1 ,y 1 ,t-Δt)，x 1 ，y 1 The position of the highest point of the error time sequence correlation;
when the correlation coefficient is between the weak correlation and the strong correlation, the correction is performed by a linear fusion mode:
α＝-2.5ρ+2 (4)
alpha is a variable which decreases from 1 to 0, and when ρ is less than or equal to 0.4, a correction error is calculated through S1; along with the improvement of the correlation coefficient, the error movement mode is gradually clear, and the weight of the S2 component is improved; when rho is more than or equal to 0.8, S2 is completely adopted to calculate correction errors.
7. The global accurate prediction system for photovoltaic power generation output according to claim 6, wherein in the prediction error coordinate marking unit, the prediction error P err The formula of (2) is as follows:
P err ＝(P pre -P real )/P N (1)
wherein P is pre To predict power, P real To actually measure power, P N Is the rated power of the photovoltaic power station.
8. The global accurate prediction system for photovoltaic power generation output according to claim 7, wherein in the prediction error estimation unit at the next time, the prediction error at the next time is:
P′ err (x,y,t+Δt)＝P err (x-v x Δt,y-v y Δt,t) (2)
in the formula, v x And v y Representing the components of wind speed in the x and y directions, respectively.
9. The global accurate prediction system for photovoltaic power generation output according to claim 7, wherein in the prediction error correction unit at the next time, the correction value of the prediction error at the next time is as follows:
P p ′ re (t+Δt)＝P pre (t+Δt)+P e ′ rr (t+Δt)·P N (5)。
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