CN111815021B - Photovoltaic power prediction method based on solar radiation climate characteristic identification - Google Patents
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Abstract
The invention relates to a photovoltaic power prediction method based on solar radiation climate characteristic identification, which comprises the following steps: 1) Defining a weather type index SCF, and classifying and dividing weather state data corresponding to different times according to the weather type index SCF; 2) Respectively selecting a slope radiation model for predicting the incident total radiation of the slope according to each type of weather state data; 3) Constructing a photovoltaic cell model to predict the direct current power generation of the photovoltaic array; 4) And constructing an inverter model, calculating the alternating current power generation power of the photovoltaic array according to the predicted value of the direct current power generation power of the photovoltaic array, and completing the prediction of the photovoltaic power. Compared with the prior art, the method has the advantages of flexible application scene, weather type identification, accuracy improvement, power prediction error reduction and the like.
Description
Technical Field
The invention relates to the field of photovoltaic power generation evaluation and prediction, in particular to a photovoltaic power prediction method based on solar radiation climate characteristic recognition.
Background
According to the statistics of the national energy bureau, a new nationwide photovoltaic power generation machine 3011 kilokilowatts is added in 2019, wherein a new centralized photovoltaic machine 1791 kilowatts; the distributed photovoltaic new installation is 1220 kilowatts, which is increased by 41.3 percent in the same ratio. The integrated machine for photovoltaic power generation reaches 20430 kilowatts, and the integrated machine is increased by 17.3% in the same ratio, wherein the integrated photovoltaic 14167 kilowatts is increased by 14.5% in the same ratio; distributed photovoltaic 6263 kilowatts, 24.2% increase comparably. From the layout of the newly-added machine, the newly-added machine capacity in 2019 of North China, northwest China, east China and south China is larger, and the newly-added machine capacity respectively accounts for 28.5%, 21.6%, 17.5% and 15.7% of the whole country.
In recent years, with the increase of grid-connected photovoltaic permeability, demands on radiation and photovoltaic power are continuously increased, students at home and abroad begin to accelerate the research on photovoltaic power generation forecasting methods, and photovoltaic power generation forecasting technologies widely adopted at home and abroad at present can be divided into the following categories:
(1) A principle forecasting method based on a solar total radiation forecasting and photoelectric efficiency conversion model;
(2) According to historical data, forecasting factor data and photovoltaic power generation data of the photovoltaic power station, adopting statistical algorithms such as multiple regression, a support vector machine and the like to carry out analysis modeling, and then inputting a numerical model forecasting result into a dynamic-statistical forecasting method;
(3) A simulation forecasting method based on a simulation model of total solar radiation and a photovoltaic I/V curve.
However, the existing photovoltaic power generation forecasting technology has the following defects:
1. The existing photovoltaic power prediction technology is difficult to be suitable for different places, and any inclination angle and azimuth angle (orientation) are adopted.
2. In the existing photovoltaic power prediction technology, the connection effect of a power prediction link and a radiation prediction link is poor.
3. Most of the existing weather type division adopts a single index definition index kt or a correction definition index kt' or total cloud quantity as a classification standard, and influences of other factors on the weather type are ignored.
4. Existing inverter efficiency models, such as simple constant models, are subject to large errors at low dc power inputs (corresponding to low solar radiation inputs).
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide a photovoltaic power prediction method based on solar radiation climate characteristic identification.
The aim of the invention can be achieved by the following technical scheme:
A photovoltaic power prediction method based on solar radiation climate characteristic identification comprises the following steps:
1) Defining a weather type index SCF, and classifying and dividing weather state data corresponding to different times according to the weather type index SCF;
2) Respectively selecting a slope radiation model for predicting the incident total radiation of the slope according to each type of weather state data;
3) Constructing a photovoltaic cell model to predict the direct current power generation of the photovoltaic array;
4) And constructing an inverter model, calculating the alternating current power generation power of the photovoltaic array according to the predicted value of the direct current power generation power of the photovoltaic array, and completing the prediction of the photovoltaic power.
Further, in the step 1), the weather state data in the step 1) includes historical data of geographic latitude, weather, solar radiation and photovoltaic output, and the corrected sharpness index and the horizontal plane direct incidence ratio calculated according to the astronomical geographic weather factors, and the photovoltaic output includes output direct current power and output alternating current power.
Further, in the step 1), before classifying and dividing, the weather state data is screened according to the solar altitude angle, when the solar altitude angle is greater than 10 degrees, classifying and dividing is performed, otherwise, data screening is performed until the solar altitude angle is greater than 10 degrees, and classifying and dividing is performed.
Further, in the step 1), the weather type index SCF is related to the corrected sharpness index k' T, the horizontal plane direct ratio B d, and the total cloud cover C, and the expression is:
SCF＝ω1Bd+ω2k′T+ω3 (1-C)
wherein ω 1、ω2、ω3 is the weight and the sum is 1, and the specific value is calculated according to the local geographic location and the radiation data.
Further, in the step 1), the weather types in which the weather state data are classified and divided are 4 types, and the abundance of direct radiation components of the solar radiation is sequentially increased, specifically:
Type 1: SCF is more than or equal to 0 and less than 0.10, and the weather condition is worst, which represents the weather conditions of light weather, gust weather, light weather, haze weather, medium weather and/or medium weather and above weather;
type 2: SCF is more than or equal to 0.10 and less than 0.25, and the weather conditions are poor, which represent cloudy, cloudy-cloudy and cloudy-cloudy weather conditions;
type 3: SCF is more than or equal to 0.25 and less than 0.55, and weather conditions are good, and represent sunny cloudy or cloudy weather conditions;
Type 4: SCF is more than or equal to 0.55 and less than 1, and the best weather condition represents the weather condition of sunny days.
Further, in the step 3), the inclined plane radiation model includes a Reindl model and a Liu & Jordan model, when the type of the weather is type 1 or type 2, the Reindl model is selected to calculate the total incident radiation of the inclined plane of the photovoltaic array, and when the type of the weather is type 3 or type 4, the Liu & Jordan model is selected to calculate the total incident radiation of the inclined plane of the photovoltaic array.
Further, the definition index may be used to represent attenuation of solar radiation by the atmosphere, and is a weather type classification index which is preferably considered, the greater the definition index is, the higher the transparency of the atmosphere is, the less attenuation of solar radiation by the atmosphere is, the greater the solar radiation reaching the ground is, and in order to reduce the influence of the solar altitude angle on the definition index, the definition index is corrected, and the expression of the corrected definition index k' T is:
Wherein k T is a definition index, m is atmospheric mass, E sc, gamma and delta are solar radiation flux correction value and declination angle of the upper boundary of the atmosphere caused by solar constant and solar-earth distance change respectively, Omega is latitude and time angle respectively, I is total radiation of the horizontal plane, and I 0 is solar radiation quantity on the outer horizontal plane of the atmosphere.
Further, the expression of the direct ratio B d (the ratio of direct radiation to total radiation of the horizontal plane) is:
Wherein I b is direct radiation in the horizontal plane, and I is total radiation in the horizontal plane.
Further, in the step 3), the expression of the photovoltaic cell model is:
Pdc＝ηPV,STC*[1-α(TC-25℃)]*It*S
Wherein η PV,STC is the photoelectric conversion efficiency of the standard test conditions, α is the temperature coefficient, and T C is the plate temperature. I t is the incident total radiation of the inclined plane of the photovoltaic array, S is the effective area of the photovoltaic array for receiving solar radiation, T a is the air temperature, NOCT is the rated solar cell working temperature, and G is the incident radiation degree of the inclined plane.
Further, in the step 4), the expression of the inverter model is:
Pac＝Pdc*ηinv*Kc
Wherein P ac is grid-connected alternating current power of the photovoltaic array, eta inv is inverter efficiency, and K c is an alternating current loop line loss coefficient.
Further, the inverter efficiency η inv is obtained by fitting a nonlinear regression model, and then:
ηinv＝a+becx(0＜a＜1,b＜0,c＜0)
a, b, c are regression equation coefficients, e is a base number of natural logarithms, x is a relative value of input direct current power of the photovoltaic array, and P N is rated installed capacity of an inverter corresponding to the photovoltaic array power generation unit.
Compared with the prior art, the invention has the following advantages:
1. the method is suitable for predicting direct current off-grid and alternating current grid-connected power at different places and at any inclination angle and azimuth angle (orientation), and has flexible application scene.
2. According to the method, weather type is effectively identified by utilizing a weather type comprehensive index SCF (related to a horizontal plane direct incidence ratio B d, a corrected definition index k' T and a total cloud cover C), an inclined plane radiation model is organically connected with a power prediction part, an optimal scheme is screened out, and the accuracy of power prediction is improved.
3. The method uses a nonlinear regression inverter efficiency model based on historical photovoltaic output data, and avoids the situation of larger power prediction error under the condition of low power input.
Drawings
Fig. 1 is an error analysis of each typical slope radiation model under different weather types, wherein fig. 1a is an error analysis of each typical slope radiation model under weather type 1, fig. 1b is an error analysis of each typical slope radiation model under weather type 2, fig. 1c is an error analysis of each typical slope radiation model under weather type 3, and fig. 1d is an error analysis of each typical slope radiation model under weather type 4.
FIG. 2 is a graph showing the comparison of predicted and measured AC power values for one year, one day and one hour in Wuhan, city, calculated using the present invention.
Fig. 3 is a flow chart of the method of the present invention.
Detailed Description
The invention will now be described in detail with reference to the drawings and specific examples.
Examples
The invention provides a photovoltaic power prediction method based on solar radiation climate characteristic recognition, which is based on a principle prediction method, and comprises the steps of screening a typical inclined plane radiation model under each weather type based on weather type recognition, calculating output direct current power and alternating current power prediction values by using a photovoltaic cell model and an inverter efficiency model, and carrying out error analysis on each step of model.
As shown in fig. 3, the present invention specifically includes the following steps:
1) Dividing weather types:
the clarity index k T represents the transparency degree of the atmosphere, is closely related to weather conditions and solar radiation, and has the formula:
The calculation formula of the solar radiation quantity I 0 on the outer horizontal plane of the atmosphere is as follows:
wherein, Omega is latitude and time angle respectively, E sc, gamma and delta are correction value and declination angle of solar radiation flux of the upper atmosphere caused by solar constant and solar earth distance change respectively, and the calculation formulas are as follows:
ESC＝1367±7W/m2
However, the sharpness index is not only related to meteorological conditions, but also to the position of the sun in the sky, which is modified in order to reduce the effect of the solar altitude on the sharpness index as follows:
where k' T is the corrected sharpness index and m is the atmospheric mass.
The direct horizontal radiation ratio B D is the ratio of direct horizontal radiation to total horizontal radiation, and the formula is as follows:
Wherein I b is direct radiation in the horizontal plane, and I is total radiation in the horizontal plane.
And respectively carrying out normalization processing and weighting on three data of the corrected definition index, the horizontal plane direct ratio and the total cloud cover to obtain a comprehensive index factor SCF, namely weather type index, wherein a specific calculation formula is as follows:
SCF＝ω1Bd+ω2k′T+ω3(1-C)
The sum of the weights omega 1,ω2 and omega 3 is 1, the specific numerical value is calculated according to the local geographic position and the radiation data, C is the total cloud cover, and Bd is the direct ratio of the horizontal plane.
The invention uses K-means clustering algorithm to divide different weather types according to weather type index SCF, concretely:
The method comprises the following steps:
Type 1: SCF is more than or equal to 0 and less than 0.10, and the weather condition is worst, which represents the weather conditions of light weather, gust weather, light weather, haze weather, medium weather and/or medium weather and above weather;
type 2: SCF is more than or equal to 0.10 and less than 0.25, and the weather conditions are poor, which represent cloudy, cloudy-cloudy and cloudy-cloudy weather conditions;
type 3: SCF is more than or equal to 0.25 and less than 0.55, and weather conditions are good, and represent sunny cloudy or cloudy weather conditions;
Type 4: SCF is more than or equal to 0.55 and less than 1, and the best weather condition represents the weather condition of sunny days.
2) Building a photovoltaic cell model:
When sunrise and sunset are considered, solar radiation is low, photovoltaic output is small, horizontal plane observation value and inclined plane radiation calculation error are large, and the photovoltaic array direct current power P dc is calculated according to the following formula by combining the photovoltaic array inclined plane incidence total radiation corresponding to various weather types obtained above:
Pdc＝ηPV,STC*[1-α(TC-25℃)]*It*S
Wherein η PV,STC is the photoelectric conversion efficiency of the standard test condition, the crystalline silicon battery is 12% -18%, the amorphous silicon film battery is 5% -8%, STC represents the standard test condition of the ground photovoltaic module, namely, the atmospheric mass AM=1.5, the incident total radiation I t =1000W/m 2 of the inclined plane of the photovoltaic array of the standard test solar battery, the panel temperature Tc=25°, the zenith angle θ Z =48.2°, and alpha is the temperature coefficient, and the solar battery material is related;
t C is the plate temperature, and the model is:
Wherein T a is the air temperature, and the unit is; NOCT is rated solar cell operating temperature, usually takes 41-48 ℃, and when NOCT=48 ℃, deltaT/Wm 2＝0.035℃/Wm2, G is inclined plane incidence radiance, the unit W/m 2, S is effective area of solar radiation received by the photovoltaic array, the unit m 2It is total solar array inclined plane incidence radiance, the unit kW/m 2 is used for carrying out classified prediction according to weather types through a photovoltaic cell model, and the method specifically comprises the following steps:
If the current weather type is1, 2, selecting Reindl model to calculate the total radiation incident on the inclined plane;
If the current weather type is 3,4, the Liu & Jordan model is selected to calculate the total incident radiation on the incline.
In this example, several typical inclined plane radiation models are selected for comparison, namely an isotropic Liu & Jordan model, an anisotropic Temps & Clouson model, a Perez model, an Erbs model, a Hay model and a Reindl model, and calculation, analysis and inspection are carried out through inclined plane total radiation actual measurement data with an inclined angle of 40 degrees in Wuhan city for a certain year. Under weather type 1, temps & Clouson model error is maximum, reindl model error is minimum, and Reindl model is preferably selected; under the weather type 2, temps & Clouson model error is maximum, reindl model error is minimum, and Reindl model is preferably selected; under the weather type 3, the error between the Erbs model and the Temps & Clouson model is larger, the error between the Liu & Jordan model is the smallest, and the Liu & Jordan model is preferably selected; under weather type 4, temps & Clouson model error is the largest, liu & Jordan model error is the smallest, and Liu & Jordan model is preferred.
Pairs of models for different weather types are shown in fig. 1, wherein MAPE is also called mean absolute percentage error, and the smaller the result is, the smaller the error is, and the more accurate the model is; NRMSE, also known as normalized root mean square error, describes the dispersion of the predicted and observed values, with smaller results indicating more accurate models; MBE is also known as average error, with smaller results indicating smaller errors.
3) Calculating inverter model parameters:
the photovoltaic power generation direct current power obtained by calculation of the photovoltaic cell model is used for calculating the photovoltaic array alternating current power, and the calculation formula is as follows:
Pac＝Pdc*ηinv*Kc
wherein, P ac is grid-connected alternating current power of the photovoltaic array, the invention uses a nonlinear regression model, and the specific formula is as follows:
ηinv＝a+becX(0＜a＜1,b＜0,c＜0)
Wherein η inv is the inverter efficiency, dimensionless; a, b, c are regression equation coefficients; e is the base of natural logarithm; x is the relative value of the input direct current power of the photovoltaic array, and is dimensionless; and P N is the rated installed capacity of the inverter corresponding to the photovoltaic array power generation unit.
Error comparison is carried out on the alternating current power obtained by calculation of the model and the calculation result of a simple constant model, wherein the time period of the simple constant model is 10:00-16:00h, and the inverter efficiency is 0.95; taking 0.8 for the rest period; k c is an ac loop line loss factor, typically about 0.95.
4) Photovoltaic power prediction:
s1, acquiring historical data of geographic latitude, weather, solar radiation and photovoltaic output (including output direct current power and output alternating current power), and calculating parameters such as correction definition index, direct ratio and the like according to the astronomical geographic weather factors;
s2: obtaining a weather type index SCF according to the horizontal plane direct incidence ratio B D and the corrected definition index k' T;
S3: judging whether the solar altitude is greater than 10 degrees, if so, entering a step S4, otherwise, returning to the step S2 after data screening;
s4: performing weather classification, namely performing data classification by using a K-means clustering algorithm according to a weather type index SCF, wherein the classification result is shown in a table 1;
TABLE 1 weather type division based on SCF index
| SCF index | Weather type |
| 0≤SCF＜0.10 | 1 |
| 0.10≤SCF＜0.25 | 2 |
| 0.25≤SCF＜0.55 | 3 |
| 0.55≤SCF＜1 | 4 |
S5: calculating total inclined plane radiation by adopting a typical inclined plane radiation model, selecting an optimal model under each weather type as shown in table 2 and fig. 1, obtaining an inclined plane total radiation predicted value, and performing error comparison with a result classified by using only a single index k' T as shown in fig. 2;
TABLE 2 error analysis of optimal ramp radiation models for different weather types
| Weather of | Inclined plane radiation model | MAPE | NRMSE | MBE |
| 1 | Reindl | 8.10％ | 13.60％ | 0.040 |
| 2 | Reindl | 18.80％ | 140.2％ | 0.307 |
| 3 | Liu&Jordan’ | 20.00％ | 25.00％ | 0.406 |
| 4 | Liu&Jordan’ | 22.30％ | 26.30％ | 0.612 |
S6, substituting the predicted value of the total radiation of the inclined plane into a calculation formula of the direct current generation power of the photovoltaic array, calculating the output direct current power, and performing error analysis, as shown in a table 3;
TABLE 3 error analysis of predicted DC power
| MAPE | rRMSE | rMBE | |
| Predicting DC power error | 36.31％ | 12.80％ | 0.033131 |
S7, calculating three parameters of a nonlinear regression model by utilizing output direct current power and alternating current power historical data to obtain inverter efficiency;
And S8, substituting the output direct current power into an inverter efficiency model to obtain output alternating current power, and comparing the result with a calculation result using a simple constant model, as shown in table 4.
Table 4 error analysis to predict grid-tie ac power
| MAPE | rRMSE | rMBE | |
| Simple constant model | 39.83％ | 10.71％ | 0.036382 |
| Nonlinear regression model | 37.18％ | 9.19％ | 0.03193 |
Claims (5)
1. The photovoltaic power prediction method based on solar radiation climate characteristic identification is characterized by comprising the following steps of:
1) Defining a weather type index SCF, and classifying and dividing weather state data corresponding to different times according to the weather type index SCF;
2) Respectively selecting a slope radiation model for predicting the incident total radiation of the slope according to each type of weather state data;
3) Constructing a predicted photovoltaic array direct current power generation;
4) Constructing an inverter model, calculating and obtaining alternating current power generation power of the photovoltaic array according to the predicted value of the direct current power generation power of the photovoltaic array, and completing prediction of the photovoltaic power;
In the step 1), the weather type index SCF is related to the corrected sharpness index k' T, the horizontal plane direct ratio B d and the total cloud cover C, and the expression is as follows:
SCF＝ω1Bd+ω2k′T+ω3(1-C)
Wherein ω 1、ω2、ω3 is a weight and the sum is 1;
In order to reduce the influence of the solar altitude angle on the definition index, the definition index is corrected, and the expression of the corrected definition index k' T is:
Wherein k T is a definition index, m is atmospheric mass, E sc, gamma and delta are solar radiation flux correction value and declination angle of the upper boundary of the atmosphere caused by solar constant and solar-earth distance change respectively, Omega is latitude and time angle respectively, I is total radiation of a horizontal plane, and I 0 is solar radiation quantity on the horizontal plane outside the atmosphere;
in the step 3), the expression of the photovoltaic cell model is as follows:
Pdc＝ηPV,STC*[1-α(TC-25℃)]*It*S
Wherein eta PV,STC is the photoelectric conversion efficiency of standard test conditions, alpha is the temperature coefficient, T C is the plate temperature, I t is the incident total radiation of the inclined plane of the photovoltaic array, S is the effective area of the photovoltaic array for receiving solar radiation, T a is the air temperature, NOCT is the rated solar cell working temperature, and G is the incident radiation degree of the inclined plane;
In the step 4), the expression of the inverter model is:
Pac＝Pdc*ηinv*Kc
Wherein P ac is grid-connected alternating current power of the photovoltaic array, eta inv is inverter efficiency, and K c is an alternating current loop line loss coefficient;
The inverter efficiency eta inv is obtained by fitting a nonlinear regression model, and is as follows:
ηinv＝a+becx(0<a<1,b<0,c<0)
a, b, c are regression equation coefficients, e is a base number of natural logarithms, x is a relative value of input direct current power of the photovoltaic array, and P N is rated installed capacity of an inverter corresponding to the photovoltaic array power generation unit.
2. The method according to claim 1, wherein in the step 1), the weather status data includes historical data of geographical latitude, weather, solar radiation and photovoltaic output, and the photovoltaic output includes output dc power and output ac power, and the correction sharpness index and the horizontal plane direct ratio.
3. The method for predicting the photovoltaic power based on the solar radiation climate characteristic recognition according to claim 1, wherein in the step 1), before classifying and classifying, weather state data are screened according to a solar altitude, classifying and classifying are performed when the solar altitude is greater than 10 degrees, and otherwise, data screening is performed until the solar altitude is greater than 10 degrees.
4. The photovoltaic power prediction method based on solar radiation climate feature recognition according to claim 1, wherein in the step 1), weather types in which weather state data are classified and divided are 4 types, specifically:
Type 1: SCF is more than or equal to 0 and less than 0.10, and the weather condition is worst, which represents the weather conditions of light weather, gust weather, light weather, haze weather, medium weather and/or medium weather and above weather;
type 2: SCF is more than or equal to 0.10 and less than 0.25, and the weather conditions are poor, which represent cloudy, cloudy-cloudy and cloudy-cloudy weather conditions;
type 3: SCF is more than or equal to 0.25 and less than 0.55, and weather conditions are good, and represent sunny cloudy or cloudy weather conditions;
Type 4: SCF is more than or equal to 0.55 and less than 1, and the best weather condition represents the weather condition of sunny days.
5. The method for predicting the photovoltaic power based on the solar radiation climate characteristic recognition according to claim 4, wherein in the step 2), the inclined plane radiation model comprises a Reindl model and a Liu & Jordan model, when the weather type is 1 type and 2 type, the Reindl model is selected to calculate the total incident radiation of the inclined plane of the photovoltaic array, and when the weather type is 3 type and 4 type, the Liu & Jordan model is selected to calculate the total incident radiation of the inclined plane of the photovoltaic array.
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