CN104156776A - Solar resource assessment method - Google Patents

Abstract

The invention relates to a solar resource assessment method. The method includes the steps of (1) establishing a regional solar resource map to obtain a solar irradiance data set S in an area; (2) building a measured data set Q; (3) building a data subset and calculating a weighting coefficient for each grid point; and (4) correcting an irradiance data set SP in the next 24h generated by a numerical weather predication model, and establishing a solar resource real-time distribution map. Considering the screening of the measured data, the solar resource assessment method can obtain relatively accurate regional solar resource distribution with as less sites as possible so as to save equipment installation and data storage resources. The linear relation and weighted average are integrated for calculating a correction coefficient, the amount of calculation is reduced, and the speed of calculation is improved. According to the invention, the real-time monitoring data and numerical weather prediction results are combined to make assessment results more accurate.

Description

A kind of solar energy resources appraisal procedure

Technical field

The present invention relates to a kind of method of evaluating the energy, specifically relate to a kind of method of evaluating solar energy source.

Background technology

Under the pressure of severe energy substitution situation and global warming, countries in the world are all using the clean energy resource that develops sustainable development as following energy development strategy, wherein sun power with aboundresources, do not have boundary line, region, the particular advantages such as clean to form one of focus of paying close attention to into people, various countries formulate grand solar energy power generating developing goal one after another.

China has a vast territory, and solar energy resources is very abundant.The more rich region of sun power accounts for the more than 2/3 of area, and a year radiant quantity exceedes 6,000,000,000 joules/square metre, and the sun power that annual earth's surface absorbs is about as much as the energy of 1.7 trillion tons of standard coal equivalents, has good sun power and utilizes condition.In order to promote the exploitation of regenerative resource, China has formulated " People's Republic of China's Renewable Energy Law " formal enforcement in 1 day January in 2006.2007, country promulgated again " planning of regenerative resource Long-and Medium-term Development ", proposed to strive the year two thousand twenty, made China's regenerative resource consumption figure account for total energy consumption ratio and reached 15%.This planning has proposed concrete developing goal to regenerative resources such as wind energy, biomass energy, sun power, water energy.Wherein, the target of solar electrical energy generation is, reaches on the basis of 300,000 kilowatts total volume in 2010, and the year two thousand twenty reaches 1,800,000 kilowatts.

Solar energy resources Real-Time Monitoring and assessment be can be to solar energy development planning and Electric Power Network Planning provides reliable foundation, collect conclusive resource data in monitoring, on the solar energy resources situation of assessment area and the basis of characteristic distributions, in conjunction with land resource situation and local electrical network condition, can contribute to find as early as possible problem and the bottleneck in solar energy resources exploitation, to formulate rational exploitation scale and to utilize scheme, thereby guarantee economy and actual operability that solar energy resources develops, guarantee the health coordinated development of solar energy power generating and electrical network.Solar energy resources research station can monitor the more solar energy resources data of system, and time scale is shorter, and data are more accurate, will provide data basis for photovoltaic generation power prediction.Need the appraisal procedure of the solar energy resources that a set of Real-Time Monitoring and power prediction are provided for this reason.

Summary of the invention

For the deficiencies in the prior art, the invention provides a kind of solar energy resources appraisal procedure, the method can be used for planning and design and photovoltaic plant operation and the scheduling of solar photovoltaic power plant.First the method utilizes mesoscale numerical weather prediction model, the region solar energy resources distribution plan calculating in conjunction with the measured data such as historical measurement data, satellite data; Then consider region solar energy resources and photometry website distribution situation, and the feature such as the topography and geomorphology in region, applicable actual measurement monitoring point selected.Finally utilize Real-time Monitoring Data to revise the solar energy resources forecast result in region, the solar energy resources that obtains real-time change distributes.

The object of the invention is to adopt following technical proposals to realize:

A kind of solar energy resources appraisal procedure, its improvements are, described method comprises

(1) set up region solar energy resources collection of illustrative plates, obtain sun power irradiance data collection S in region;

(2) build measured data collection Q;

(3) build data subset, calculate the weighting coefficient of each net point;

(4) the irradiance data collection S of the following 24h that correction numerical weather prediction model generates _p, set up solar energy resources real-time distribution figure.

Preferably, described step (1) comprises global context field, region terrain data and surface vegetation data input mesoscale numerical weather prediction model, set up localized numerical weather prediction model and select Parameterization Scheme, formation zone sun power irradiance distribution figure, obtains the sun power irradiance data collection S in region.

Further, described Parameterization Scheme comprises Microphysical scheme, long-wave radiation transmission plan, shortwave radiation transmission plan, skin lamination, land surface scheme, boundary layer scheme and Convective parameterization schemes.

Preferably, described step (2) comprises data pre-service and data screening.

Further, described data pre-service comprises

The photometric data that a, screening are collected;

B, rejecting or revise unreasonable data, obtain photometric data collection P.

Further, described step b comprises

6.1) deletion error or exceptional value;

6.2) time that has time shift to exist by following formula correction:

p(t)=p(t-m)；

Wherein, p (t) is the photometric data in t moment, and m is time shift length.

Further, described data screening comprises the steps;

7.1) calculate the correlativity between measured data:

$r_{\mathrm{xy}}=\frac{\mathrm{\Σ}(x_i-\stackrel{\‾}{x})(x_i-\stackrel{\‾}{y})}{\sqrt{\mathrm{\Σ}{(x_i-\stackrel{\‾}{x})}^2\mathrm{\Σ}{(y_i-\stackrel{\‾}{y})}^2}}$

R in formula _xyfor relative coefficient, x, y are photometric data sequence, with be respectively the arithmetic mean of sequence x and y;

7.2) data screening

R _xy>=0.9, and the distance of two photometric data points is while being less than 10km, from photometric data collection P, picks out data integrity rate lower, or the data sequence higher with other data sequence correlativitys;

7.3) generate measured data collection Q

Data sequence in data set P is screened, pick out redundant data sequence, obtain measured data collection Q.

Preferably, described step (3) comprises

8.1) the sun power irradiance data collection S obtaining from step (1), extract with step (2) in the data subset S ' of monitoring location coordinate place grid in measured data collection Q, and the time interval of data sequence in S ' is identical with the concentrated corresponding data of Q;

8.2) ask for the linear correlation coefficient A and the deviation ratio B of Q and S ' by following formula,

S′＝AQ+B

Wherein, n is the number of data sequence in measured data collection Q;

8.3) be calculated as follows the weighting coefficient of m net point in region, computing method are as follows:

$\begin{array}{cc}{k}_a\left(m\right)=\frac{\underset{i=1}{\overset{n}{\mathrm{\Σ}}}{k}_i{a}_i}{\underset{i=1}{\overset{n}{\mathrm{\Σ}}}{k}_i}& k_b\left(m\right)=\frac{\underset{i=1}{\overset{n}{\mathrm{\Σ}}}{k}_i{b}_i}{\underset{i=1}{\overset{n}{\mathrm{\Σ}}}{k}_i}\end{array}$

Wherein k _i=1/l _i, l _ithat the center of grid m is to the distance of each measured data position;

8.4) by step 8.3) weighting coefficient of zoning net point.

Preferably, described step (4) comprises

9.1) by the irradiance data SP of the following 24h of each net point of numerical weather prediction model formation zone;

9.2) revise S _p(t), obtain the solar energy resources distribution S in t moment _p' (t).The irradiance in the t moment of m net point is:

S _P′(m,t)＝k _a(m)S _P(m,t)+k _b(m)；

9.3) the irradiance distribution figure of drawing area.

Compared with the prior art, beneficial effect of the present invention is:

1, the present invention has provided concrete steps and the flow process of the solar energy resources assessment based on Real-Time Monitoring, has very strong operability and application value.

2, the present invention has considered the screening to measured data, can realize obtaining more accurate region solar energy resources with the least possible website and distributing, and installs and data storage resource with saving equipment.

3, the present invention comprehensively adopts the linear correlation and weighted mean to calculate correction factor, has reduced calculated amount, has improved computing velocity.

4, compared with traditional solar energy resources appraisal procedure, the present invention combines Real-time Monitoring Data with numerical weather forecast result, makes the result of assessment more accurate.

Brief description of the drawings

Fig. 1 is a kind of solar energy resources appraisal procedure process flow diagram provided by the invention.

Fig. 2 is mesoscale numerical weather prediction model WRF mode computation process flow diagram provided by the invention.

Embodiment

Below in conjunction with accompanying drawing, the specific embodiment of the present invention is described in further detail.

1, set up region solar energy resources collection of illustrative plates

By data input mesoscale climate model WRF patterns such as global context field and landform, surface vegetations, by twice, yardstick falls, set up localized numerical weather prediction model, select suitable Parameterization Scheme, the historical sun power irradiance distribution figure of formation zone, obtains the sun power irradiance data collection S in this region.The calculation process of WRF pattern is as accompanying drawing 2, and in pattern, the main Parameterization Scheme of considering is as shown in table 1.

The main Parameterization Scheme of considering in table 1 pattern

?	Parameterization Scheme title
		1	Microphysical scheme (mp_physics)
2	Long-wave radiation transmission plan (ra_lw_physics)

3	Shortwave radiation transmission plan (ra_sw_physics)
		4	Skin lamination (sf_sfclay_physics)
5	Land surface scheme (sf_surface_physics)
		6	Boundary layer scheme (bl_pbl_physics)
7	Convective parameterization schemes (cu_physics)

2, light simultaneous measurement data processing

1) data pre-service

(1) photometric data of collecting is screened, mark unreasonable or abnormal data, and to its classification, unreasonable and abnormal data can be divided into following three classes conventionally: the error in data that 1) data acquisition mistake or data transmission channel fault cause; 2) equipment or communication failure and preserve the improper shortage of data causing; 3) due to data collecting card time calibration with the data time shift that deviation is brought is set.

(2) reject and revise unreasonable data, finally obtain photometric data collection P, be mainly divided into the following steps:

A) deletion error and exceptional value;

B) to there are the data of time shift, carry out time correction:

p(t)＝p(t-m)

Wherein p (t) is the photometric data in t moment, and m is time shift length.

Wherein, calculate the percentage of head rice k that revises rear data:

If data integrity rate k<85%, or there is the continuous data disappearance that exceedes 1 week, to mark explanation to data set.

2) data screening

A) calculate the correlativity between measured data

$r_{\mathrm{xy}}=\frac{\mathrm{\&Sigma;}(x_i-\stackrel{\&OverBar;}{x})(x_i-\stackrel{\&OverBar;}{y})}{\sqrt{\mathrm{\&Sigma;}{(x_i-\stackrel{\&OverBar;}{x})}^2\mathrm{\&Sigma;}{(y_i-\stackrel{\&OverBar;}{y})}^2}}$

R in formula _xyfor relative coefficient, x, y are photometric data sequence, with be respectively the arithmetic mean of sequence x and y.

B) data screening

If r _xy>=0.9, and the distance of two photometric data points is less than 10km, from photometric data collection P, picks

Go out the data sequence that data integrity rate is lower or higher with other data sequence correlativitys.

C) generate measured data collection Q

After in data set P, all data sequences are screened, pick out redundant data sequence, obtain the measured data collection Q that subsequent analysis is used.

3, set up solar energy resources real-time distribution figure

1) determine correction factor

A) the sun power irradiance data collection S obtaining from step 1, extract with measured data collection Q in the data subset S ' of monitoring location coordinate place grid, and the time interval of data sequence in S ' is identical with the concentrated corresponding data of Q.

B) ask for Q and S ' the linear correlation coefficient A and deviation ratio B, formula is as follows:

S′＝AQ+B

Wherein, n is the number of data sequence in measured data collection Q.

C) weighting coefficient of m net point in zoning, computing method are as follows:

$\begin{array}{cc}{k}_a\left(m\right)=\frac{\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}{k}_i{a}_i}{\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}{k}_i}& k_b\left(m\right)=\frac{\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}{k}_i{b}_i}{\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}{k}_i}\end{array}$

Wherein k _i=1/l _i, l _ithat the center of grid m is to the distance of each measured data position.

D) repeating step (c), calculates the weighting coefficient of all net points in whole region.

2) the solar energy resources real-time distribution of formation zone

A) by the irradiance data S of the following 24h of each net point of WRF numerical weather prediction model formation zone _p.

B) to S _p(t) revise, obtain the solar energy resources distribution S in t moment _p' (t).The irradiance in the t moment of m net point is:

S _P′(m,t)＝k _a(m)S _P(m,t)+k _b(m)；

C) according to the irradiance result of each net point, the irradiance distribution figure of drawing area.

Finally should be noted that: above embodiment is only in order to illustrate that technical scheme of the present invention is not intended to limit, although the present invention is had been described in detail with reference to above-described embodiment, those of ordinary skill in the field are to be understood that: still can modify or be equal to replacement the specific embodiment of the present invention, and do not depart from any amendment of spirit and scope of the invention or be equal to replacement, it all should be encompassed in the middle of claim scope of the present invention.

Claims (9)

1. a solar energy resources appraisal procedure, is characterized in that, described method comprises

(1) set up region solar energy resources collection of illustrative plates, obtain sun power irradiance data collection S in region;

(2) build measured data collection Q;

(3) build data subset, calculate the weighting coefficient of each net point;

(4) the irradiance data collection S of the following 24h that correction numerical weather prediction model generates _p, set up solar energy resources real-time distribution figure.

2. a kind of solar energy resources appraisal procedure as claimed in claim 1, it is characterized in that, described step (1) comprises global context field, region terrain data and surface vegetation data input mesoscale numerical weather prediction model, set up localized numerical weather prediction model and select Parameterization Scheme, formation zone sun power irradiance distribution figure, obtains the sun power irradiance data collection S in region.

3. a kind of solar energy resources appraisal procedure as claimed in claim 2, it is characterized in that, described Parameterization Scheme comprises Microphysical scheme, long-wave radiation transmission plan, shortwave radiation transmission plan, skin lamination, land surface scheme, boundary layer scheme and Convective parameterization schemes.

4. a kind of solar energy resources appraisal procedure as claimed in claim 1, is characterized in that, described step (2) comprises data pre-service and data screening.

5. a kind of solar energy resources appraisal procedure as claimed in claim 4, is characterized in that, described data pre-service comprises

The photometric data that a, screening are collected;

B, rejecting or revise unreasonable data, obtain photometric data collection P.

6. a kind of solar energy resources appraisal procedure as claimed in claim 5, is characterized in that, described step b comprises

6.1) deletion error or exceptional value;

6.2) time that has time shift to exist by following formula correction:

p(t)=p(t-m)；

Wherein, p (t) is the photometric data in t moment, and m is time shift length.

7. a kind of solar energy resources appraisal procedure as claimed in claim 4, is characterized in that, described data screening comprises the steps;

7.1) calculate the correlativity between measured data:

$r_{\mathrm{xy}}=\frac{\mathrm{\&Sigma;}(x_i-\stackrel{\&OverBar;}{x})(x_i-\stackrel{\&OverBar;}{y})}{\sqrt{\mathrm{\&Sigma;}{(x_i-\stackrel{\&OverBar;}{x})}^2\mathrm{\&Sigma;}{(y_i-\stackrel{\&OverBar;}{y})}^2}}$

R in formula _xyfor relative coefficient, x, y are photometric data sequence, with be respectively the arithmetic mean of sequence x and y;

7.2) data screening

R _xy>=0.9, and the distance of two photometric data points is while being less than 10km, from photometric data collection P, picks out data integrity rate lower, or the data sequence higher with other data sequence correlativitys;

7.3) generate measured data collection Q

Data sequence in data set P is screened, pick out redundant data sequence, obtain measured data collection Q.

8. a kind of solar energy resources appraisal procedure as claimed in claim 1, is characterized in that, described step (3) comprises

8.1) the sun power irradiance data collection S obtaining from step (1), extract with step (2) in the data subset S ' of monitoring location coordinate place grid in measured data collection Q, and the time interval of data sequence in S ' is identical with the concentrated corresponding data of Q;

8.2) ask for the linear correlation coefficient A and the deviation ratio B of Q and S ' by following formula,

S′=AQ+B

Wherein, n is the number of data sequence in measured data collection Q;

8.3) be calculated as follows the weighting coefficient of m net point in region, computing method are as follows:

$\begin{array}{cc}{k}_a\left(m\right)=\frac{\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}{k}_i{a}_i}{\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}{k}_i}& k_b\left(m\right)=\frac{\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}{k}_i{b}_i}{\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}{k}_i}\end{array}$

Wherein k _i=1/l _i, l _ithat the center of grid m is to the distance of each measured data position;

8.4) by step 8.3) weighting coefficient of zoning net point.

9. a kind of solar energy resources appraisal procedure as claimed in claim 1, is characterized in that, described step (4) comprises

9.1) by the irradiance data S of the following 24h of each net point of numerical weather prediction model formation zone _p;

9.2) revise S _p(t), obtain the solar energy resources distribution S in t moment _p' (t).The irradiance in the t moment of m net point is:

S _P′(m,t)＝k _a(m)S _P(m,t)+k _b(m)；

9.3) the irradiance distribution figure of drawing area.