CN110610262B - Long-time scale photovoltaic time sequence output generation method considering weather elements - Google Patents
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Abstract
The utility model discloses a long-time scale photovoltaic time sequence output generation method considering weather elements, which comprises the following steps of: s1, generating solar radiation data with constant atmospheric transparency; s2, correcting the atmospheric transparency of the photovoltaic installation site by using the recorded data of the NASA; s3, generating time sequence weather change data by using a Monte Carlo method and correcting the solar radiation intensity; s4, generating a photovoltaic time sequence active output curve according to the output characteristic curve of the photovoltaic panel; s5, adjusting buckling coefficients according to the number of annual utilization hours; the method can enable the final power supply configuration scheme to be more in line with the actual situation and meet the requirement of power supply reliability when the power supply planning research of the photovoltaic equipment is carried out; the power supply configuration scheme has a good improvement effect on the power supply reliability.
Description
Technical Field
The utility model is suitable for the field of power supply optimization planning of photovoltaic equipment, and particularly relates to a power supply optimization method for power supply reliability evaluation.
Background
In order to cope with environmental problems, global warming, and achieving sustainable development, clean energy must be greatly developed, and photovoltaic power generation has been greatly developed. When the accumulated installed capacity of the photovoltaic reaches a higher level, the uncertainty output of the photovoltaic needs to be effectively counted when the power supply optimization planning of the photovoltaic-containing equipment is carried out, and particularly when the power supply reliability of a power supply scheme is evaluated, the photovoltaic output data with a long time scale is often needed. However, since photovoltaic power generation has volatility, it is affected by uncontrollable factors such as day-night alternation, weather changes, seasonal changes, etc., which cause severe changes in photovoltaic output, it is necessary to predict or generate a time-series output of photovoltaic based on a physical or mathematical method.
There are many methods for predicting solar radiation intensity, and the existing methods can be roughly classified into three types: 1) Physical methods, namely, calculating according to the relative position of the sun and the earth and the atmospheric transparency of the earth atmosphere; 2) The sampling method generally considers that the solar radiation obeys the Beta distribution, so that the solar radiation can be realized by random number sampling according to the Beta distribution; 3) The prediction class method uses a support vector machine, a neural network, a fuzzy theory and other methods. The method is mainly aimed at short-term solar radiation intensity prediction, and when generating photovoltaic time sequence output with long time scale, the existing method often has the following defects: 1) The solar radiation intensity data obtained by the sampling method are independent, continuous change of weather cannot be reflected, the irregular characteristic of the sampling data is quite mild compared with long-time continuous weak radiation existing in reality due to overcast and rainy weather, and the solar radiation intensity data based on the sampling method makes the power supply planning have larger error with the actual peak capacity adjustment requirement; 2) The prediction type method generally requires data of solar radiation intensity, ambient temperature, atmospheric humidity, etc. in a time interval of hours (or less) for a sufficient period of time as raw data, and these data are difficult to obtain, and time-series solar radiation intensity data obtained by the prediction type method are also difficult to judge the error range.
Disclosure of Invention
Aiming at the technical problems in the prior art, the utility model provides a long-time scale photovoltaic time sequence output generation method considering weather elements, which can lead the final power supply configuration scheme to be more in line with the actual situation and meet the requirement of power supply reliability when the power supply planning research of photovoltaic equipment is carried out; the power supply configuration scheme has a good improvement effect on the power supply reliability.
In order to solve the problems in the prior art, the utility model adopts the following technical scheme:
a long-time scale photovoltaic time sequence output generation method considering weather elements comprises the following steps:
s1, collecting statistical data such as longitude and latitude, average monthly radiance, historical weather statistical information, annual utilization hours of photovoltaic equipment and the like of the place where the photovoltaic equipment is located;
s2, respectively calculating solar direct radiation and scattered radiation when the transparency of the atmosphere is constant according to latitude data of the place where the photovoltaic equipment is located, and finally obtaining solar total radiation data;
s3, extracting solar radiation intensity historical data counted by month in the NASA database, and performing per unit treatment to obtain
S4, extracting solar radiation intensity month-average value from S2 and performing per unit treatment to obtain +.>
Correction according to the set step size->
Up to after last correction +>
And->
The same, finally get->
S5, generating time sequence weather change data by using a Monte Carlo sampling method, converting the weather change data into a discount coefficient transparent to the atmosphere, and correcting
And obtain corrected solar radiation vector I' 1 ；
S6, generating a photovoltaic time sequence output vector according to an output characteristic curve of the photovoltaic panel;
s7, calculating the annual utilization hours C corresponding to the photovoltaic time sequence output vector according to the convergence criterion of the Monte Carlo method 1 ；
S8, counting the annual utilization hours of the photovoltaic equipment at the photovoltaic installation site std To this end, WF is modified in steps of a predetermined length until the number of hours of annual use C of the final photovoltaic device final And C std The difference of (2) is smaller than the set value epsilon', and the target photovoltaic output vector is obtained.
S5, adjusting the transparency of the atmosphere according to discount coefficients corresponding to weather conditions one by one:
5.1 according to the worldThe recorded data of the air network are used for dispersing all weather conditions into sunny, cloudy, rain, snow, sand and dust and other seven types, and the number of days when the weather conditions appear is recorded as a vector W, W= [ W ] 1 ,w 2 ,…,w 7 ]Wherein the subscripts correspond to the seven weather types in turn;
5.2 assuming that the durations of seven weather are all subject to a lognormal distribution, the mean and standard deviation of the seven weather durations are denoted as vectors M and D, respectively, then m= [ M ] 1 ,m 2 ,...,m 7 ]，D＝[d 1 ,d 2 ,...,d 7 ]. The probability of occurrence of seven weather conditions is denoted as the vector P, p= [ P ] w,1 ,p w,2 ,…,p w,7 ]The method comprises the steps of carrying out a first treatment on the surface of the The probability distribution vector corresponding to seven kinds of weather is marked as a vector P ', P ' = [ P ] ' w,1 ,p′ w,2 ,…,p′ w,7 ]Wherein
5.3 influence coefficients of seven kinds of weather of the photovoltaic installation place on the solar radiation intensity are recorded as vectors WF, WF= [ WF ] 1 ,wf 2 ,…,wf 7 ]And 0 < wf i And < 1, representing the discounted coefficient of solar radiation intensity reaching the photovoltaic panel in this weather. Based on the Markov process, using M, D and P' to generate time sequence weather change data with an hour interval in one year, further obtaining time sequence data of influence of seven kinds of weather on solar radiation intensity in one year according to WF, and marking the time sequence data as a vector SW, wherein SW= { SW 1 ,sw 2 ,…,sw 8760 }，sw i ∈{wf 1 ,wf 2 ,…,wf 7 }，
From SW and->
Can obtain time sequence solar radiation intensity data I' 1 ，I′ 1 ＝[I′ 1,1 ,I′ 1,2 ,…,I′ 1,8760 ]Wherein
Advantageous effects
Firstly, the utility model takes the long time scale time sequence output vector of the photovoltaic unit as a research object, and only 3 types of easily obtained statistical data are required to be input when the utility model is used: the longitude and latitude data of the photovoltaic installation site, the statistical data of the average illumination intensity of the NASA to the photovoltaic installation site and the historical weather data from the global weather network can obtain long-time scale photovoltaic output data considering day and night changes, seasonal changes and weather elements, and further the time sequence photovoltaic output data is applied to the optimization problems of power supply planning and the like. The periodic change along with sunrise and sunset, the change of the overall trend along with month or season and the change of the height along with weather change are fully considered, so that the generated time sequence output curve has higher accuracy, the reliability of the power supply planning scheme is greatly improved, and the method has better engineering application value.
Secondly, through effective treatment of the transparency parameter of the atmosphere, irregular changes of the photovoltaic output are highlighted, periodic changes of the photovoltaic output along with sunrise and sunset, integral trend changes along with months or seasons and continuous day changes along with weather changes can be simultaneously counted, and annual utilization hours of the photovoltaic equipment can be adjusted according to local illumination statistical data. In general, on the premise of slightly increasing the calculation difficulty and complexity, the weather elements are effectively considered, and the obtained long-time scale photovoltaic output data is closer to the actual situation.
Drawings
FIG. 1 is a flow chart of a method of generating a long time scale photovoltaic time series output taking into account weather elements in accordance with the present utility model;
FIG. 2 is a step-by-step generation of a long-time scale photovoltaic time series output time series curve of the present utility model;
fig. 3 is a graph showing the output characteristics of the photovoltaic device of the present utility model.
Detailed Description
The utility model is further described below with reference to the drawings and examples.
The long-time scale photovoltaic output refers to a photovoltaic output with the length of at least 1 year, and the implementation process of the utility model patent is further described in detail below with reference to the accompanying drawings.
As shown in fig. 1, the utility model provides a power supply optimization method for photovoltaic equipment based on weather requirements, which comprises the following steps:
s1, collecting statistical data such as longitude and latitude, average monthly radiance, historical weather statistical information, annual utilization hours of photovoltaic equipment and the like of the place where the photovoltaic equipment is located;
s2, (101, 201) respectively calculating solar direct radiation and scattered radiation when the transparency of the atmosphere is constant according to latitude data of the place where the photovoltaic equipment is located, and finally obtaining solar total radiation data;
this step generates solar radiation data with constant atmospheric transparency, including the following:
setting the transparency of the atmosphere to a certain constant value p 0 (e.g. p 0 Time sequence solar direct radiation, scattered radiation and total radiation intensity for one year are calculated by taking an hour (or shorter time, such as 1 minute) as a time interval, and longitude and latitude information of a photovoltaic device installation place is required to be input in the calculation process.
sinα s ＝sinφ*sinδ+cosφ*cosδ*cosω (1-2)
ω＝(k-12)*15 (1-3)
I b ＝r*I se *p m *sinα s (1-4)
I h ＝I b +I d (1-8)
Wherein delta represents the declination angle of the sun, delta is more than or equal to-23.44 degrees and less than or equal to 23.44 degrees; n represents the nth day of the year, assuming that n=1 represents 1 month and 1 day; alpha s Representing the solar altitude; phi represents the geographical latitude of the simulated location; omega represents the solar time angle, 0 DEG at noon, 1h every 15 DEG, negative in the morning and positive in the afternoon; i b Represents the direct solar radiation intensity of the horizontal plane, W/m 2 ；I se The solar radiation intensity representing the upper atmosphere is generally 1367W/m 2 The method comprises the steps of carrying out a first treatment on the surface of the p represents atmospheric transparency, dimensionless; m represents the atmospheric mass, dimensionless; r represents a daily correction coefficient; i d Representing the solar scattered radiation intensity in the horizontal plane, W/m 2 ；M 1 And M 2 To correspond to the transparency p of the atmosphere 0 Is dimensionless; i h Representing the total solar radiation in the horizontal plane, W/m 2 。
When the time interval is the hour, the time sequence solar energy total radiation vector 8760h in the whole year can be obtained by utilizing the formula
The solar radiation intensity timing curve with constant atmospheric transparency as shown in FIG. 2, panel a, is not part of the core innovation work herein, in particularCan be referred to in reference [1 ]]. This step results in a solar radiation vector as shown in fig. 2. A.
S3, (301) extracting solar radiation intensity historical data counted by month in NASA database, and performing per unit treatment to obtain
The step extracts the recorded data of the NASA and performs per unit processing, and comprises the following contents:
historical average of solar radiation intensity of photovoltaic installation site according to month is recorded as vector I sd (available through NASA website query), I sd ＝[I sd,1 ,I sd ,2,...,I sd,12 ]The method comprises the steps of carrying out a first treatment on the surface of the According to
The total amount of radiation per month for 12 months is obtained, denoted +.>
Correction->
Up to and including I sd The per unit value of (2) is the same.
S4, (401) extracting solar radiation intensity month average value from S2 and performing per unit treatment to obtain
Correction according to the set step size->
Up to after last correction +>
And->
The same, finally get->
The step aims at the NASA according to the historical statistical data of months, adjusts the solar radiation intensity, and comprises the following contents:
to be used for
The average atmospheric transparency per month and the average atmospheric transparency per day are adjusted for the target so that +.>
And->
To simplify the simulation, it is assumed herein that the daily atmospheric transparency remains unchanged. The atmospheric transparency on day 15 of each month is denoted as p i,15 I e {1,2, …,12}. Search->
And->
The most differentiated element, the corresponding sequence number is denoted as k, k e {1,2, …,12}:
p k,15 ＝p k,15 +Δp (1-13)
wherein Δp represents p i,15 The correction step length of the formula (1-12) and the formula (1-13) ensures
Atmospheric transparency p according to day 15 of 12 months i,15 The atmospheric transparency for each day can be calculated using a linear interpolation method, which is not extended here.
Recalculating the solar total radiation timing vector change using the corrected atmospheric transparency
Corresponding total amount of month radiation +.>
Then search for +.>
And->
The element with the greatest difference is repeated for the correction process until the +.>
And->
And->
And->
All elements differ by less than a certain set point epsilon (e.g., epsilon=0.01). This step results in a solar radiation vector as shown in fig. 2. B.
S5, (501, 601) generating time sequence weather change data by using Monte Carlo sampling method, converting the weather change data into discount coefficient transparent to atmosphere, and correcting
And obtain corrected solar radiation vector I' 1 ；
The method comprises the steps of generating time sequence weather change data by using a Monte Carlo method, and correcting solar radiation intensity according to atmospheric transparency discount coefficients corresponding to the weather data one by one, wherein the specific contents are as follows:
according to the recorded data of the global weather network, all weather conditions are scattered into sunny, cloudy, rain, snow, sand and dust and other seven types, and the number of days of occurrence of the weather conditions is recorded as a vector W, and W= [ W ] 1 ,w 2 ,…,w 7 ]Wherein the subscripts correspond in turn to the seven weather types described above.
Assuming that the durations of seven weather are all subject to a lognormal distribution, the average and standard deviation of the seven weather durations are recorded as vectors M and D, respectively, then m= [ M ] 1 ,m 2 ,…,m 7 ]，D＝[d 1 ,d 2 ,…,d 7 ]. The probability of occurrence of seven weather conditions is denoted as the vector P, p= [ P ] w,1 ,p w,2 ,…,p w,7 ]The method comprises the steps of carrying out a first treatment on the surface of the The probability distribution vector corresponding to seven kinds of weather is marked as a vector P ', P ' = [ P ] ' w,1 ,p′ w,2 ,…,p′ w,7 ]Wherein
The influence coefficient of seven kinds of weather on the solar radiation intensity of the photovoltaic installation place is recorded as a vector WF, WF= [ WF ] 1 ,wf 2 ,…,wf 7 ]And 0 < wf i < 1, indicating that the photovoltaic panel is reached in such weatherDiscount coefficient of solar radiation intensity of (c). Based on the Markov process, using M, D and P' to generate time sequence weather change data with an hour interval in one year, further obtaining time sequence data of influence of seven kinds of weather on solar radiation intensity in one year according to WF, and marking the time sequence data as a vector SW, wherein SW= { SW 1 ,sw 2 ,…,sw 8760 }，sw i ∈{wf 1 ,wf 2 ,…,wf 7 }，
From SW and I tlast Can obtain time sequence solar radiation intensity data I' 1 ，I′ 1 ＝[I′ 1,1 ,I′ 1,2 ,…,I′ 1,8760 ]Wherein
This step results in a solar radiation vector as shown in fig. 2. C.
S6, (701) generating a photovoltaic time sequence output vector according to an output characteristic curve of the photovoltaic panel;
the real-time output of the photovoltaic mainly depends on illumination intensity, and the relation between the output and the light intensity of the photovoltaic array in the model is shown in fig. 3 and consists of a nonlinear area, a linear area and a constant area.
S7, (801) calculating the annual utilization hour number C corresponding to the photovoltaic time sequence output vector according to the convergence criterion of the Monte Carlo method 1 ；
The method comprises the steps of calculating the annual utilization hours corresponding to the preliminarily generated long-time scale photovoltaic output data, wherein the specific contents are as follows:
generating a time sequence photovoltaic output vector of N (N is 10000) years according to the steps, and recording as
The annual utilization hours of the photovoltaic are statistical values of data of N years, and the annual utilization hours of the ith (i is more than or equal to 1 and less than or equal to N) are as follows:
the average of the annual hours of use is:
s8, (901) annual utilization hour count value C of photovoltaic equipment at photovoltaic installation site std To this end, WF is modified in steps of a predetermined length until the number of hours of annual use C of the final photovoltaic device final And C std The difference of (2) is smaller than the set value epsilon', and the target photovoltaic output vector is obtained.
The method comprises the following steps of correcting a long-time scale photovoltaic output vector according to a year utilization hour count value, wherein the method comprises the following specific contents:
since WF is a set amount, C 1 The number of hours C of use of the photovoltaic panel actual to the site being simulated std There are differences, hereinafter referred to as C std To adjust WF to standard, finally, the annual utilization hours and C std The same applies. According to the difference of research targets or the difference of regions, the wf can be sequentially adjusted by adopting the same or different step length in the adjustment process i The utility model adopts the same adjustment step length:
the time sequence output vector of the photovoltaic panel after the first adjustment is formed by
Become->
Corresponding annual hours of utilizationAverage value is from C 1 Becomes C 2 The method comprises the steps of carrying out a first treatment on the surface of the Then repeating the adjustment process until final adjustment C final And C std The difference between (a) is smaller than a certain set value epsilon '(e.g., epsilon' =3). Recording the final adjusted WF to obtain the photovoltaic cell time-series power curve shown in FIG. 2. D.
It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the utility model, which are all within the scope of the utility model. Accordingly, the scope of protection of the present utility model is to be determined by the appended claims.
Claims (1)
1. The long-time scale photovoltaic time sequence output generation method considering weather elements is characterized by comprising the following steps of:
s1, collecting statistical data such as longitude and latitude, average monthly radiance, historical weather statistical information, annual utilization hours of photovoltaic equipment and the like of the place where the photovoltaic equipment is located;
s2, respectively calculating solar direct radiation and scattered radiation when the transparency of the atmosphere is constant according to latitude data of the place where the photovoltaic equipment is located, and finally obtaining solar total radiation data;
s3, extracting solar radiation intensity historical data counted by month in the NASA database, and performing per unit treatment to obtain
S4, extracting solar radiation intensity month-average value from S2 and performing per unit treatment to obtain
Correction according to the set step size->
Up to after last correction +>
And->
The same, finally get->
S5, generating time sequence weather change data by using a Monte Carlo sampling method, converting the weather change data into a discount coefficient transparent to the atmosphere, and correcting
And obtain corrected solar radiation vector I' 1 The method comprises the steps of carrying out a first treatment on the surface of the S5, adjusting the transparency of the atmosphere according to discount coefficients corresponding to weather conditions one by one:
5.1, dispersing all weather conditions into sunny, cloudy, rain, snow, sand and other seven types according to the recorded data of the global weather network, and recording the days of occurrence of the seven types as a vector W, wherein W= [ W ] 1 ,w 2 ,…,w 7 ]Wherein the subscripts correspond to the seven weather types in turn;
5.2 assuming that the durations of seven weather are all subject to a lognormal distribution, the mean and standard deviation of the seven weather durations are denoted as vectors M and D, respectively, then m= [ M ] 1 ,m 2 ,…,m 7 ]，D＝[d 1 ,d 2 ,…,d 7 ]. The probability of occurrence of seven weather conditions is denoted as the vector P, p= [ P ] w,1 ,p w,2 ,…,p w,7 ]The method comprises the steps of carrying out a first treatment on the surface of the The probability distribution vector corresponding to seven kinds of weather is marked as a vector P ', P ' = [ P ] ' w,1 ,p′ w,2 ,…,p′ w,7 ]Wherein
5.3 influence coefficients of seven kinds of weather of the photovoltaic installation place on the solar radiation intensity are recorded as vectors WF, WF= [ WF ] 1 ,wf 2 ,…,wf 7 ]And 0 is<wf i <1, representing a discount coefficient of the intensity of solar radiation reaching the photovoltaic panel in such weather;
based on the Markov process, using M, D and P' to generate time sequence weather change data with an interval of hours in one year, further obtaining time sequence data of influence of seven kinds of weather on solar radiation intensity in one year according to WF, and recording as a vector SW, wherein SW= { SW 1 ,sw 2 ,…,sw 8760 }，sw i ∈{wf 1 ,wf 2 ,…,wf 7 }，
From SW and->
Can obtain time sequence solar radiation intensity data I' 1 ，I′ 1 ＝[I′ 1,1 ,I′ 1,2 ,…,I′ 1,8760 ]Wherein:
s6, generating a photovoltaic time sequence output vector according to an output characteristic curve of the photovoltaic panel;
s7, calculating the annual utilization hours C corresponding to the photovoltaic time sequence output vector according to the convergence criterion of the Monte Carlo method 1 ；
S8, counting the annual utilization hours of the photovoltaic equipment at the photovoltaic installation site std To this end, WF is modified in steps of a predetermined length until the number of hours of annual use C of the final photovoltaic device final And C std The difference of (2) is smaller than the set value epsilon', and a target photovoltaic output vector is obtained, wherein:
since WF is a set amount, C 1 Actual with the simulated siteThe photovoltaic panel of (2) uses the number of hours C std There are differences, hereinafter referred to as C std To adjust WF to standard, finally, the annual utilization hours and C std The same;
according to the difference of research targets or the difference of regions, the wf can be sequentially adjusted by adopting the same or different step length in the adjustment process i The utility model adopts the same adjustment step length:
the time sequence output vector of the photovoltaic panel after adjustment is represented by
Become->
The corresponding annual average value of the annual average hours is represented by C 1 Becomes C 2 The method comprises the steps of carrying out a first treatment on the surface of the Repeating the adjustment process until final adjustment C final And C std The difference between (2) is smaller than a set value epsilon'.
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