typical meteorological year report for csp, cpv and pv solar plants

TECHNICAL REPORT

TMY

October 18th , 2011

SOLAR RESOURCE ASSESSMENT: of 17

TECHNICAL REPORT:

SOLAR RESOURCE ASSESSMENT

DATE:

October 18th, 2011

AUTHORS:

IrSOLaV S. L. (INVESTIGACIONES Y RECURSOS SOLARES AVANZADOS).

CUSTOMER:


INDEX

1 INTRODUCTION ........................................................................................................................................... 6

1.1 Main objective .......................................................................................................................................... 6

1.2 Data needed ............................................................................................................................................ 6

1.3 Methodology for solar radiation derived from satellite images ................................................................ 7

1.4 Brief summary of IrSOLaV methodology ................................................................................................. 7

2 TYPICAL SOLAR RADIATION YEAR FOR PROJECT .............................................................................. 10

2.1 Global and direct radiation values ......................................................................................................... 12

3 STATISTICAL ANALYSIS OF THE LONG TERM ...................................................................................... 14

4 REFERENCES ............................................................................................................................................ 15


1 INTRODUCTION

1.1 Main objective

The main objective of this report is to analyze the solar resource available and to produce the

corresponding typical solar radiation year for a specific site in -, selected for hosting a solar thermal

power plant. The solar resource analysis applies to a site with geographic coordinates: Latitude x S,

Longitude x E, and located in the province of Northern Cape, hereinafter referred to as PROJECT.

1.2 Data needed

The solar radiation is a meteorological variable measured only in few measurement stations and

during short and, on most occasions, discontinuous periods of times. The lack of reliable information

on solar radiation, together with the spatial variability that it presents, leads to the fact that

developers do not find appropriate historical databases with information available on solar resource

for concrete sites. This lack provokes in turn serious difficulties at the moment of projecting or

evaluating solar power systems.

Among the possible different approaches to characterize the solar resource of a given specific site

they can be pointed out the following:

Data from nearby stations. This option can be useful for relatively flat terrains and when

distances are less than 10 km far from the site. In the case of complex terrain or longer

distances the use of radiation data from other geographical points is absolutely

inappropriate.

Interpolation of surrounding measurements. This approach can be only used for areas with a

high density of stations and for average distances between stations of about 20-50 km

[Pérez et al., 1997; Zelenka et al., 1999].

Solar radiation estimation from satellite images is currently the most suitable approach. It supplies

the best information on the spatial distribution of the solar radiation and it is a methodology clearly

accepted by the scientific community and with a high degree of maturity [McArthur, 1998]. In this

regard, it is worth to mention that BSRN (Baseline Surface Radiation Network) has among its

objectives the improvement of methods for deriving solar radiation from satellite images, and also

the Experts Working Group of Task 36 of the Solar Heating and Cooling Implement Agreement of

IEA (International Energy Agency) focuses on solar radiation knowledge from satellite images.


1.3 Methodology for solar radiation derived from satellite images

Solar radiation derived from satellite images is based upon the establishment of a functional

relationship between the solar irradiance at the Earth’s surface and the cloud index estimated from

the satellite images. This relationship has been previously fitted by using high quality ground data, in

such a manner that the solar irradiance-cloud index correlation can be extrapolated to any location

of interest and solar radiation components can be calculated from the satellite observations for that

point.

1.4 Brief summary of IrSOLaV methodology

The methodology of IrSOLaV uses two main inputs to compute hourly solar irradiance: the

geostationary satellite images and the information about the attenuating properties of the

atmosphere. The former consists of one image per hour offering information related with the cloud

cover characteristics. The latter is basically information on the daily Linke turbidity which is a very

representative parameter to model the attenuating processes which affects solar radiation on its

path through the atmosphere, mainly the aerosol optical depth and water vapor column.

The methodology applied has undoubtedly been accepted by the scientific community and its main

usefulness is in the estimation of the spatial distribution of solar radiation over a region. Its maturity

is guaranteed by initiatives like the establishment in 2004 of a new IEA (International Energy

Agency) task known as “Solar Radiation Knowledge from Satellite Images” or the fact that the

measuring solar radiation network BSRN (Baseline Surface Radiation Network) promoted by WMO

(World Meteorological Organization) has as its main objectives for the improvement of solar

radiation estimation from satellite images models.

Solar radiation estimation from satellite images offered is made from a modified version of the

renowned model Heliosat-3, developed and validated by CIEMAT with more than thirty radiometric

stations in the Iberian Peninsula. Over this first development, IrSOLaV has generated a tool fully

operational which is applied on a database of satellite images available with IrSOLaV (temporal and

spatial resolution of the data depends on the satellite covering the region under study). It is

worthwhile to point out that tuning-up and fitting of the original methodology in different locations of

the World have been performed and validated with local data from radiometric stations installed in

the region of interest. This way, it may be considered that the treatment of the information from

satellite images offered by IrSOLaV is an exclusive service.

Even though the different research groups working in this field are making use of the same core

methodologies, there are several characteristics that differ depending on the specific objectives


pursued. Therefore, the main differences between the IrSOLaV/CIEMAT and others, like the ones

applied by PVGis or Helioclim are:

Selection of the working window. The correlations developed by IrSOLaV/CIEMAT are

focused on the Iberian Peninsula, and in particular in Spain, making use of 30 equidistant

meteo-stations in this territory. However the other groups use stations distributed among all

Europe and the resulting relations are applied to all the territory.

Filtering of images and terrestrial data. Images and data used for the fitting and relations are

thoroughly filtered with procedures developed specifically for this purpose.

Selection of albedo for clear sky. The algorithm used for selection of clear sky albedos

provides a daily sequence that is different for every year, however the other methodologies

use a unique monthly value.

Introduction of characteristic variables. The relation developed by IrSOLaV/CIEMAT includes

new variables characterizing the climatology of the site and the geographical location, with a

significant improvement of the results obtained for global and direct solar radiation.

The uncertainty of the estimation comparing with hourly ground pyranometric measurements is

expressed in terms of the relative root mean squared error (RMSE). Different assessments and

benchmarking tests can been found at the available literature concerning the use of satellite images

(Meteosat and GOES) on different geographic sites and using different models [Pinker y Ewing,

1985; Zelenka et al., 1999; Pereira et al., 2003; Rigollier et al., 2004; Lefevre et al., 2007]. The

uncertainty for hourly values is estimated around 20-25% RMSE and in a daily basis the uncertainty

of the models used to be about 13-17%. It is important to mention here the contribution given by

Zelenka in terms of distributing the origin of this error, concluding that 12-13% is produced by the

methodology itself converting satellite information into radiation data and a relevant fraction of 7-

10% because of the uncertainty of the ground measurements used for the comparison. In addition

Zelenka estimates that the error of using nearby ground stations beyond 5 km reaches 15%.

Because of that his conclusion is that the use of hourly data from satellite images is more accurate

than using information from nearby stations located more than 5 km far from the site.

The IrSOLaV methodology is based on the work developed in CIEMAT by the group of Solar

Radiation Studies. The model has been assessed for about 30 Spanish sites with the following

uncertainty data for global horizontal irradiance:

About 12% RMSE for hourly values

Less than 10% for daily values


Less than 5% for annual and monthly means

The model has been modified for a better estimation of solar radiation with clear sky, leading to an

important improvement in the accuracy of the model [Polo, 2009; Polo et al., 2009b]. This improved

model is the one applied to the PROJECT site in this report.


2 TYPICAL SOLAR RADIATION YEAR FOR PROJECT

Simulation of solar thermal power production systems is a tool of high interest in various phases of

design and development of any project. They will require, among others, climatological data to

define the climatic environment in which the site is located. The approaches to the definition of the

climatic environment have evolved in line with the requirements of the different simulation programs

and the availability of climatic data.

A first approximation may be the annual series of hourly values called Short Reference Year (Lund,

1985) available to several European countries (Lund, 1985). This type of time information cannot be

considered "sufficiently typical" versus so-called Typical Meteorological Year (TMY) due mainly to

the requirements / availability of data needed and the selection method used.

The TMY is formed by a set of hourly data including solar radiation, temperature, humidity and wind,

over a 1-year period, that is, 8760 records of the main climatic variables. It is formed, in the strict

sense, by the concatenation of selected months from specific years (i.e., January '96 + February '97

+ March '02, etc). The criterion used for the selection of these months is adopted according to their

applicability for the simulation of solar systems (concentrated solar systems, CPC, PV, buildings).

Therefore, it can be said that there is no standard method for their generation. In any case,

depending on the necessities of the end-user of the TMY and data availability, it is possible to

choose the fair method from the one suggested by different authors and published in the scientific

journals. Whatever the method used for getting the TMY of the site, it should be representative of

the climatic evolution of the different variables included.

From the dataset available for the site of PROJECT, hourly data of solar radiation over a period of

12 years, a modified version of the empirical methodology proposed by the Sandia National

Laboratories (Hall et al., 1978) is applied. The basic requirements for the use of such a method are:

Databases with global solar irradiation over horizontal surface, dry bulb temperature and any

of the variables that define the moisture content of the atmosphere (relative humidity, wet

bulb temperature, dew temperature ,...), direction and wind speed.

Sampling period equal to or less than one hour.

Database with a minimum of 10 years.

In the method proposed by Hall the hourly data available are processed and a daily database is

built, consisting of 13 meteorological parameters:


Ambient temperature (maximum, minimum, average and variation).

Relative humidity or temperature or dew-wet temperature (maximum, minimum,

average, and oscillation).

Wind speed (maximum, minimum, average, and oscillation).

Cumulated global solar radiation on horizontal surface.

Each month of the year is examined separately and the TMY is formulated in the same way or

applying same methodology as TMM (Typical Meteorological Month). The process to obtain the

TMMs is done by comparing the cumulative frequency distribution function (CDF) of each parameter

for a given month (i.e. January 2000) with the CDF corresponding to the set of all similar months of

the whole period (i.e. January 2000-2010). The comparison is performed using the Finkelstein-

Schafer statistic, FS, that quantifies the discrepancy for any particular month versus the set of the

same month for all the years (Filkenstein and Schafer, 1971).

1

1 n

i

i

FSn

(1)

Where i is the absolute difference between the CDF for a particular month and the CDF for the set

of same months and n is the number of days of such a month.

FS equation is calculated for each month and for each of the 13 parameters. Since these

parameters are not equally important, appropriate weighting factors, wj, are applied to each of them

and a statistical value for the whole set is calculated, WS, as the following weighted sum:

13

1

j j

j

WS w FS

(2)

The weighting factors for each one of the 13 analyzed parameters depend on the type of application

to be given to the TMY. A "typical month" is considered that one who minimizes the statistic WS.

The methodology presented is one of the most commonly used during the last twenty years


[Pissimanis et al., 1988; Zarzalejo et al., 1995; de Miguel y Bilbao, 2005; Yang et al., 2007] for all

types of systems depending on the weighting factors chosen.

2.1 Global and direct radiation values

For the selection of the typical year for the site of PROJECT, only the solar radiation variable is

used. Table 11 shows the results produced by the statistical WS for the three years that better

characterize the series. From this information it is possible to select the "typical month" among all

available months.

Table 1: WS statistical standard for the three best candidates

Year/Month JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC

1994 0.231 0.335 0.205

1995 0.086

1996 0.273

1997 0.295

1998 0.132 0.138

1999 0.210 0.207 0.233

2000 0.115 0.061 0.171 0.278 0.058

2001 0.120

2002 0.204 0.094

2003 0.222 0.202

2004 0.191 0.129 0.104 0.217 0.302

2005 0.161 0.286

2006 0.117 0.149 0.108

2007 0.123

2008 0.309

2009 0.240 0.215

2010 0.251

The final selection corresponds to the following sequence of months/years:

TMY= {01/2007, 02/2000, 03/2004, 04/2002, 05/2003, 06/2000,

07/2001, 08/2000, 09/2003, 10/1999, 11/1994, 12/2000}

Table 12 shows monthly daily-average values of the months selected to build the TMY and the

values corresponding to the average of 17 years. The same data is plotted in Figure 13, where it can


be seen how the profiles of the TMY fit adequately to the average profiles of the whole series, in

both global radiation on horizontal surface as direct radiation on normal surface.

Table 2: Daily values of the months selected for the TMY and average of 17 years (kWh m-2

day-1

). Total

values in (kWh m-2

year-1

)

Global Direct

Month Year TMY AVG TMY AVG

JAN 2007 7.41 7.49 7.31 7.31

FEB 2000 6.91 6.78 6.79 6.67

MAR 2004 6.01 5.96 6.43 6.37

APR 2002 5.09 5.03 6.43 6.44

MAY 2003 4.30 4.22 6.81 6.57

JUN 2000 3.94 3.88 6.96 6.8

JUL 2001 4.20 4.19 7.11 7.09

AUG 2000 5.19 5.16 7.66 7.55

SEP 2003 6.03 6.25 7.28 7.7

OCT 1999 6.99 7.09 7.45 7.62

NOV 1994 7.64 7.82 8.08 8.18

DEC 2000 8.08 8.18 8.18 8.52

Total 2175 2193 2625 2621

Figure 13: Distribution of monthly daily-average (kWh m-2

day-1

) of TMY and database IrSOLaV (1994-2010).

for GHI and DNI irradiance.


3 STATISTICAL ANALYSIS OF THE LONG TERM

The long term analysis of the solar resource for PROJECT is based on the estimations of the

complementary values to the percentiles. Let’s denote Pi as the i-th percentile of a given population.

Thus, P75 is the 75-percentile of the sample and it means that the probability of finding a value

equal or less than P75 is just 75%. The complementary of the percentile will be denoted as pi, in

such a way that p75 will represent the same value as P25, and the meaning is that the probability of

finding a value equal or higher than p75 is just the 75%. A statistical analysis based upon the p

values in the sense of complementary values to the percentiles is presented in this section.

The p50, p75 and p90 of the monthly values for global horizontal and direct normal irradiation are

shown in Table 13. It can be pointed out the proximity of the p50 values with the TMY estimates.

Table 3: Monthly values of p50, p75 and p90 for global horizontal and direct normal irradiation (kWh m-2

day-1

) and their corresponding yearly values (kWh m

-2 year

-1)

P50 P75 P90

Month G DNI G DNI G DNI

JAN 7.43 7.18 7.00 6.41 6.94 6.25

FEB 6.88 6.79 6.47 5.92 6.04 5.32

MAR 6.08 6.53 5.62 5.44 5.38 5.25

APR 5.02 6.43 4.86 6.07 4.52 5.25

MAY 4.20 6.64 4.14 6.20 3.95 5.82

JUN 3.94 6.96 3.83 6.56 3.64 6.23

JUL 4.20 7.14 4.09 6.84 3.94 6.35

AUG 5.19 7.59 5.02 7.11 4.92 6.91

SEP 6.27 7.57 6.05 7.31 5.85 6.98

OCT 7.07 7.53 6.97 7.33 6.73 7.02

NOV 7.95 8.28 7.60 7.82 7.32 7.09

DEC 8.17 8.68 8.05 8.07 7.75 7.72

Yearly 2192 2649 2111 2460 2029 2313


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typical meteorological year report for csp, cpv and pv solar plants

Engineering

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solar resource analysis

solar resource assessment

hourly solar irradiance