Package 'solaR'

Description

The solaR package allows for reproducible research both for photovoltaics (PV) systems performance and solar radiation. It includes a set of classes, methods and functions to calculate the sun geometry and the solar radiation incident on a photovoltaic generator and to simulate the performance of several applications of the photovoltaic energy. This package performs the whole calculation procedure from both daily and intradaily global horizontal irradiation to the final productivity of grid-connected PV systems and water pumping PV systems.

Details

solaR is designed using a set of S4 classes whose core is a group of slots with multivariate time series. The classes share a variety of methods to access the information and several visualization methods. In addition, the package provides a tool for the visual statistical analysis of the performance of a large PV plant composed of several systems.

Although solaR is primarily designed for time series associated to a location defined by its latitude/longitude values and the temperature and irradiation conditions, it can be easily combined with spatial packages for space-time analysis.

The best place to learn how to use the package is the companion paper published by the Journal of Statistical Software:

Perpiñán Lamigueiro, O. (2012). solaR: Solar Radiation and Photovoltaic Systems with R. Journal of Statistical Software, 50(9), 1–32. https://doi.org/10.18637/jss.v050.i09

Please note that this package needs to set the timezone to UTC. Every ‘zoo’ object created by the package will have an index with this time zone as a synonym of mean solar time..

You can check it after loading solaR with:

Sys.getenv('TZ')

If you need to change it, use:

Sys.setenv(TZ = 'YourTimeZone')

Index of functions and classes:

G0-class                Class "G0": irradiation and irradiance on the
                        horizontal plane.
Gef-class               Class "Gef": irradiation and irradiance on the
                        generator plane.
HQCurve                 H-Q curves of a centrifugal pump
Meteo-class             Class "Meteo"
NmgPVPS                 Nomogram of a photovoltaic pumping system
ProdGCPV-class          Class "ProdGCPV": performance of a grid
                        connected PV system.
ProdPVPS-class          Class "ProdPVPS": performance of a PV pumping
                        system.
Shade-class             Class "Shade": shadows in a PV system.
Sol-class               Class "Sol": Apparent movement of the Sun from
                        the Earth
aguiar                  Markov Transition Matrices for the Aguiar etal.
                        procedure
as.data.frameD          Methods for Function as.data.frameD
as.data.frameI          Methods for Function as.data.frameI
as.data.frameM          Methods for Function as.data.frameM
as.data.frameY          Methods for Function as.data.frameY
as.zooD                 Methods for Function as.zooD
as.zooI-methods         Methods for Function as.zooI
as.zooM                 Methods for Function as.zooM
as.zooY                 Methods for Function as.zooY
calcG0                  Irradiation and irradiance on the horizontal
                        plane.
calcGef                 Irradiation and irradiance on the generator
                        plane.
calcShd                 Shadows on PV systems.
calcSol                 Apparent movement of the Sun from the Earth
compare                 Compare G0, Gef and ProdGCPV objects
compareLosses           Losses of a GCPV system
corrFdKt                Correlations between the fraction of diffuse
                        irradiation and the clearness index.
d2r                     Conversion between angle units.
diff2Hours              Small utilities for difftime objects.
fBTd                    Daily time base
fCompD                  Components of daily global solar irradiation on
                        a horizontal surface
fCompI                  Calculation of solar irradiance on a horizontal
                        surface
fInclin                 Solar irradiance on an inclined surface
fProd                   Performance of a PV system
fPump                   Performance of a centrifugal pump
fSolD                   Daily apparent movement of the Sun from the
                        Earth
fSolI                   Instantaneous apparent movement of the Sun from
                        the Earth
fSombra                 Shadows on PV systems
fTemp                   Intradaily evolution of ambient temperature
fTheta                  Angle of incidence of solar irradiation on a
                        inclined surface
getData                 Methods for function getData
getG0                   Methods for function getG0
getLat                  Methods for Function getLat
helios                  Daily irradiation and ambient temperature from
                        the Helios-IES database
hour                    Utilities for time indexes.
indexD                  Methods for Function indexD
indexI                  Methods for Function indexI
indexRep-methods        Methods for Function indexRep
levelplot-methods       Methods for function levelplot.
local2Solar             Local time, mean solar time and UTC time zone.
mergesolaR              Merge solaR objects
optimShd                Shadows calculation for a set of distances
                        between elements of a PV grid connected plant.
prodEx                  Productivity of a set of PV systems of a PV
                        plant.
prodGCPV                Performance of a grid connected PV system.
prodPVPS                Performance of a PV pumping system
pumpCoef                Coefficients of centrifugal pumps.
readBD                  Daily or intradaily values of global horizontal
                        irradiation and ambient temperature from a
                        local file or a data.frame.
readG0dm                Monthly mean values of global horizontal
                        irradiation.
shadeplot               Methods for Function shadeplot
solaR.theme             solaR theme
window                  Methods for extracting a time window
writeSolar              Exporter of solaR results
xyplot-methods          Methods for function xyplot in Package 'solaR'

Author(s)

Oscar Perpiñán Lamigueiro

Maintainer: Oscar Perpiñán Lamigueiro <[email protected]>

Description

Compute the apparent movement of the Sun from the Earth with the functions fSolD and fSolI.

Usage

calcSol(lat, BTd, sample = 'hour', BTi,
        EoT = TRUE, keep.night = TRUE,
        method = 'michalsky')

Arguments

Value

A Sol-class object.

Author(s)

Oscar Perpiñán Lamigueiro.

References

• Cooper, P.I., Solar Energy, 12, 3 (1969). "The Absorption of Solar Radiation in Solar Stills"

• Spencer, Search 2 (5), 172, https://www.mail-archive.com/[email protected]/msg01050.html

• Strous: https://www.aa.quae.nl/en/reken/zonpositie.html

• Michalsky, J., 1988: The Astronomical Almanac's algorithm for approximate solar position (1950-2050), Solar Energy 40, 227-235

• Perpiñán, O, Energía Solar Fotovoltaica, 2015. (https://oscarperpinan.github.io/esf/)

• Perpiñán, O. (2012), "solaR: Solar Radiation and Photovoltaic Systems with R", Journal of Statistical Software, 50(9), 1-32, doi:10.18637/jss.v050.i09

Examples

BTd = fBTd(mode = 'serie')

lat = 37.2
sol = calcSol(lat, BTd[100])
print(as.zooD(sol))

library(lattice)
xyplot(as.zooI(sol))

solStrous = calcSol(lat, BTd[100], method = 'strous')
print(as.zooD(solStrous))

solSpencer = calcSol(lat, BTd[100], method = 'spencer')
print(as.zooD(solSpencer))

solCooper = calcSol(lat, BTd[100], method = 'cooper')
print(as.zooD(solCooper))

Description

This function obtains the global, diffuse and direct irradiation and irradiance on the horizontal plane from the values of daily and intradaily global irradiation on the horizontal plane. It makes use of the functions calcSol, fCompD, fCompI, fBTd and readBD (or equivalent).

Besides, if information about maximum and minimum temperatures values are available it obtains a series of temperature values with fTemp.

Usage

calcG0(lat, modeRad = 'prom', dataRad,
       sample = 'hour', keep.night = TRUE,
       sunGeometry = 'michalsky',
       corr, f, ...)

Arguments

Value

A G0 object.

Author(s)

Oscar Perpiñán Lamigueiro.

References

• Perpiñán, O, Energía Solar Fotovoltaica, 2015. (https://oscarperpinan.github.io/esf/)

• Perpiñán, O. (2012), "solaR: Solar Radiation and Photovoltaic Systems with R", Journal of Statistical Software, 50(9), 1-32, doi:10.18637/jss.v050.i09

• Aguiar, Collares-Pereira and Conde, "Simple procedure for generating sequences of daily radiation values using a library of Markov transition matrices", Solar Energy, Volume 40, Issue 3, 1988, Pages 269–279

See Also

calcSol, fCompD, fCompI, readG0dm, readBD, readBDi, corrFdKt.

Examples

G0dm = c(2.766, 3.491, 4.494, 5.912, 6.989, 7.742, 7.919, 7.027, 5.369, 3.562, 2.814, 2.179)*1000;
Ta = c(10, 14.1, 15.6, 17.2, 19.3, 21.2, 28.4, 29.9, 24.3, 18.2, 17.2, 
  15.2)

g0 <- calcG0(lat = 37.2, modeRad = 'prom', dataRad = list(G0dm = G0dm, Ta = Ta))
print(g0)
xyplot(g0)

## Aguiar et al.

g0 <- calcG0(lat = 37.2, modeRad = 'aguiar', dataRad = G0dm)
print(g0)
xyplot(g0)

##Now the G0I component of g0 is used as
##the bdI argument to calcG0 in order to
##test the intradaily correlations of fd-kt

BDi = as.zooI(g0)
BDi$Ta = 25 ##Information about temperature must be contained in BDi

g02 <- calcG0(lat = 37.2, 
            modeRad = 'bdI', 
            dataRad = list(lat = 37.2, file = BDi), 
            corr = 'none')

print(g02)

g03 <- calcG0(lat = 37.2, 
            modeRad = 'bdI', 
            dataRad = list(lat = 37.2, file = BDi), 
            corr = 'BRL')
print(g03)

xyplot(fd ~ kt, data = g03, pch = 19, alpha = 0.3)

## Not run: 
##NREL-MIDC
##La Ola, Lanai
##Latitude: 20.76685o North
##Longitude: 156.92291o West
##Elevation: 381 meters AMSL
##Time Zone: -10.0

NRELurl <- 'http://goo.gl/fFEBN'

dat <- read.table(NRELurl, header = TRUE, sep = ',')
names(dat) <- c('date', 'hour', 'G0', 'B', 'D0', 'Ta')

##B is direct normal. We need direct horizontal.
dat$B0 <- dat$G0 - dat$D0

##http://www.nrel.gov/midc/la_ola_lanai/instruments.html:
##The datalogger program runs using Greenwich Mean Time (GMT), 
##data is converted to Hawaiin Standard Time (HST) after data collection
idxLocal <- with(dat, as.POSIXct(paste(date, hour), format = '%m/%d/%Y %H:%M', tz = 'HST'))
idx <- local2Solar(idxLocal, lon = -156.9339)

NRELMeteo <- zoo(dat[, c('G0', 'D0', 'B0', 'Ta')], idx)

lat = 20.77

g0 <- calcG0(lat = lat, modeRad = 'bdI', dataRad = NRELMeteo, corr = 'none')
xyplot(g0)
xyplot(as.zooI(g0), superpose = TRUE)

g02 <- calcG0(lat = lat, modeRad = 'bdI', dataRad = NRELMeteo, corr = 'BRL')
xyplot(g02)
xyplot(as.zooI(g02), superpose = TRUE)
xyplot(fd ~ kt, data = g02, pch = 19, cex = 0.5, alpha = 0.5)

g03 <- calcG0(lat = lat, modeRad = 'bdI', dataRad = NRELMeteo, corr = 'CLIMEDh')
xyplot(g03)
xyplot(as.zooI(g03), superpose = TRUE)
xyplot(fd ~ kt, data = g03, pch = 19, cex = 0.5, alpha = 0.5)

## End(Not run)

Description

This function obtains the global, diffuse and direct irradiation and irradiance on the generator plane from the values of daily or intradaily global irradiation on the horizontal plane. It makes use of the functions calcG0, fTheta, fInclin. Besides, it can calculate the shadows effect with the calcShd function.

Usage

calcGef(lat,
        modeTrk = 'fixed',
        modeRad = 'prom',
        dataRad,
        sample = 'hour',
        keep.night = TRUE,
        sunGeometry = 'michalsky',
        corr, f,
        betaLim = 90, beta = abs(lat)-10, alfa = 0,
        iS = 2, alb = 0.2, horizBright = TRUE, HCPV = FALSE,
        modeShd = '',
        struct = list(),
        distances = data.frame(),
        ...)

Arguments

Value

A Gef object.

Author(s)

Oscar Perpiñán Lamigueiro.

References

• Hay, J. E. and McKay, D. C.: Estimating Solar Irradiance on Inclined Surfaces: A Review and Assessment of Methodologies. Int. J. Solar Energy, (3):pp. 203, 1985.

• Martin, N. and Ruiz, J.M.: Calculation of the PV modules angular losses under field conditions by means of an analytical model. Solar Energy Materials & Solar Cells, 70:25–38, 2001.

• D. T. Reindl and W. A. Beckman and J. A. Duffie: Evaluation of hourly tilted surface radiation models, Solar Energy, 45:9-17, 1990.

• Perpiñán, O, Energía Solar Fotovoltaica, 2015. (https://oscarperpinan.github.io/esf/)

• Perpiñán, O. (2012), "solaR: Solar Radiation and Photovoltaic Systems with R", Journal of Statistical Software, 50(9), 1-32, doi:10.18637/jss.v050.i09

See Also

calcG0, fTheta, fInclin, calcShd.

Examples

lat <- 37.2

###12 Average days.

G0dm = c(2.766, 3.491, 4.494, 5.912, 6.989, 7.742, 7.919, 7.027, 5.369,
         3.562, 2.814, 2.179)*1000;
Ta = c(10, 14.1, 15.6, 17.2, 19.3, 21.2, 28.4, 29.9, 24.3, 18.2, 17.2,
       15.2)

##Fixed surface, default values of inclination and azimuth.

gef <- calcGef(lat = lat, modeRad = 'prom', dataRad = list(G0dm = G0dm, Ta = Ta))
print(gef)
xyplot(gef)

##Two-axis surface, no limitation angle.

gef2 <- calcGef(lat = lat, modeRad = 'prom',
                dataRad = list(G0dm = G0dm, Ta = Ta),
                modeTrk = 'two')
print(gef2)
xyplot(gef2)

struct = list(W = 23.11, L = 9.8, Nrow = 2, Ncol = 8)
distances = data.frame(Lew = 40, Lns = 30, H = 0)

gefShd <- calcGef(lat = lat, modeRad = 'prom',
                  dataRad = list(G0dm = G0dm, Ta = Ta),
                  modeTrk = 'two',
                  modeShd = c('area', 'prom'), 
                  struct = struct, distances = distances)
print(gefShd)

## Not run: 
##Fixed surface using Aguiar method
gefAguiar <- calcGef(lat = lat, modeRad = 'aguiar', dataRad = G0dm)

##Two-axis tracker, using the previous result.
##'gefAguiar' is internally coerced to a 'G0' object.

gefAguiar2 <- calcGef(lat = lat, modeRad = 'prev', dataRad = gefAguiar, modeTrk = 'two')
print(gefAguiar2)
xyplot(gefAguiar2)

###Shadows between two-axis trackers, again using the gefAguiar result.

struct = list(W = 23.11, L = 9.8, Nrow = 2, Ncol = 8)
distances = data.frame(Lew = 40, Lns = 30, H = 0)

gefShdAguiar <- calcGef(lat = lat, modeRad = 'prev', 
                        dataRad = gefAguiar, modeTrk = 'two', 
                        modeShd = c('area', 'prom'), 
                        struct = struct, distances = distances)
print(gefShdAguiar)

## End(Not run)

Description

Compute every step from solar angles to effective irradiance to calculate the performance of a grid connected PV system.

Usage

prodGCPV(lat,
         modeTrk = 'fixed',
         modeRad = 'prom',
         dataRad,
         sample = 'hour',
         keep.night = TRUE,
         sunGeometry = 'michalsky',
         corr, f,
         betaLim = 90, beta = abs(lat)-10, alfa = 0,
         iS = 2, alb = 0.2, horizBright = TRUE, HCPV = FALSE,
         module = list(),
         generator = list(),
         inverter = list(),
         effSys = list(),
         modeShd = '',
         struct = list(),
         distances = data.frame(),
         ...)

Arguments

Details

The calculation of the irradiance on the horizontal plane is carried out with the function calcG0. The transformation to the inclined surface makes use of the fTheta and fInclin functions inside the calcGef function. The shadows are computed with calcShd while the performance of the PV system is simulated with fProd.

Value

A ProdGCPV object.

Author(s)

Oscar Perpiñán Lamigueiro

References

• Perpiñán, O, Energía Solar Fotovoltaica, 2015. (https://oscarperpinan.github.io/esf/)

• Perpiñán, O. (2012), "solaR: Solar Radiation and Photovoltaic Systems with R", Journal of Statistical Software, 50(9), 1-32, doi:10.18637/jss.v050.i09

See Also

fProd, calcGef, calcShd, calcG0, compare, compareLosses, mergesolaR

Examples

library(lattice)
library(latticeExtra)

lat <- 37.2;

G0dm <- c(2766, 3491, 4494, 5912, 6989, 7742, 7919, 7027, 5369, 3562,
          2814, 2179)

Ta <- c(10, 14.1, 15.6, 17.2, 19.3, 21.2, 28.4, 29.9, 24.3, 18.2,
        17.2, 15.2)

prom <- list(G0dm = G0dm, Ta = Ta)

###Comparison of different tracker methods
prodFixed <- prodGCPV(lat = lat, dataRad = prom,
                      keep.night = FALSE)

prod2x <- prodGCPV(lat = lat, dataRad = prom,
                   modeTrk = 'two',
                   keep.night = FALSE)

prodHoriz <- prodGCPV(lat = lat,dataRad = prom,
                      modeTrk = 'horiz',
                      keep.night = FALSE)

##Comparison of yearly productivities
compare(prodFixed, prod2x, prodHoriz)
compareLosses(prodFixed, prod2x, prodHoriz)

##Comparison of power time series
ComparePac <- CBIND(two = as.zooI(prod2x)$Pac,
                    horiz = as.zooI(prodHoriz)$Pac,
                    fixed = as.zooI(prodFixed)$Pac)

AngSol <- as.zooI(as(prodFixed, 'Sol'))

ComparePac <- CBIND(AngSol, ComparePac)

mon <- month(index(ComparePac))

xyplot(two + horiz + fixed ~ AzS|mon, data = ComparePac,
       type = 'l',
       auto.key = list(space = 'right',
                     lines = TRUE,
                     points = FALSE),
       ylab = 'Pac')

## Not run: 
###Use of modeRad = 'aguiar' and modeRad = 'prev'
prodAguiarFixed <- prodGCPV(lat = lat,
                            modeRad = 'aguiar',
                            dataRad = G0dm,
                            keep.night = FALSE)

##We want to compare systems with different effective irradiance
##so we have to convert prodAguiarFixed to a 'G0' object.
G0Aguiar <- as(prodAguiarFixed, 'G0')

prodAguiar2x <- prodGCPV(lat = lat,
                         modeTrk = 'two',
                         modeRad = 'prev',
                         dataRad = G0Aguiar)

prodAguiarHoriz <- prodGCPV(lat = lat,
                            modeTrk = 'horiz',
                            modeRad = 'prev',
                            dataRad = G0Aguiar)

##Comparison of yearly values
compare(prodAguiarFixed,
        prodAguiar2x,
        prodAguiarHoriz)

compareLosses(prodAguiarFixed,
              prodAguiar2x,
              prodAguiarHoriz)

##Compare of daily productivities of each tracking system
compareYf <- mergesolaR(prodAguiarFixed,
                        prodAguiar2x,
                        prodAguiarHoriz)
xyplot(compareYf, superpose = TRUE,
       ylab = 'kWh/kWp',
       main = 'Daily productivity',
       auto.key = list(space = 'right'))

## End(Not run)

###Shadows
#Two-axis trackers
struct2x <- list(W = 23.11, L = 9.8, Nrow = 2, Ncol = 8)
dist2x <- data.frame(Lew = 40, Lns = 30, H = 0)
prod2xShd <- prodGCPV(lat = lat, dataRad = prom,
                      modeTrk = 'two',
                      modeShd = 'area',
                      struct = struct2x,
                      distances = dist2x)
print(prod2xShd)

#Horizontal N-S tracker
structHoriz <- list(L = 4.83);
distHoriz <- data.frame(Lew = structHoriz$L*4);

#Without Backtracking
prodHorizShd <- prodGCPV(lat = lat, dataRad = prom,
                         sample = '10 min',
                         modeTrk = 'horiz',
                         modeShd = 'area', betaLim = 60,
                         distances = distHoriz,
                         struct = structHoriz)
print(prodHorizShd)

xyplot(r2d(Beta)~r2d(w),
       data = prodHorizShd,
       type = 'l',
       main = 'Inclination angle of a horizontal axis tracker',
       xlab = expression(omega (degrees)),
       ylab = expression(beta (degrees)))

#With Backtracking
prodHorizBT <- prodGCPV(lat = lat, dataRad = prom,
                        sample = '10 min',
                        modeTrk = 'horiz',
                        modeShd = 'bt', betaLim = 60,
                        distances = distHoriz,
                        struct = structHoriz)

print(prodHorizBT)

xyplot(r2d(Beta)~r2d(w),
       data = prodHorizBT,
       type = 'l',
       main = 'Inclination angle of a horizontal axis tracker\n with backtracking',
       xlab = expression(omega (degrees)),
       ylab = expression(beta (degrees)))

compare(prodFixed, prod2x, prodHoriz, prod2xShd,
        prodHorizShd, prodHorizBT)

compareLosses(prodFixed, prod2x, prodHoriz, prod2xShd,
              prodHorizShd, prodHorizBT)

compareYf2 <- mergesolaR(prodFixed, prod2x, prodHoriz, prod2xShd,
                         prodHorizShd, prodHorizBT)

xyplot(compareYf2, superpose = TRUE,
       ylab = 'kWh/kWp', main = 'Daily productivity',
       auto.key = list(space = 'right'))

Description

Compute every step from solar angles to effective irradiance to calculate the performance of a PV pumping system.

Usage

prodPVPS(lat,
         modeTrk = 'fixed',
         modeRad = 'prom',
         dataRad,
         sample = 'hour',
         keep.night = TRUE,
         sunGeometry = 'michalsky',
         corr, f,
         betaLim = 90, beta = abs(lat)-10, alfa = 0,
         iS = 2, alb = 0.2, horizBright = TRUE, HCPV = FALSE,
         pump , H,
         Pg, converter= list(),
         effSys = list(),
         ...)

Arguments

Details

The calculation of the irradiance on the generator is carried out with the function calcGef. The performance of the PV system is simulated with fPump.

Value

A ProdPVPS object.

Author(s)

Oscar Perpiñán Lamigueiro.

References

• Abella, M. A., Lorenzo, E. y Chenlo, F.: PV water pumping systems based on standard frequency converters. Progress in Photovoltaics: Research and Applications, 11(3):179–191, 2003, ISSN 1099-159X.

• Perpiñán, O, Energía Solar Fotovoltaica, 2015. (https://oscarperpinan.github.io/esf/)

• Perpiñán, O. (2012), "solaR: Solar Radiation and Photovoltaic Systems with R", Journal of Statistical Software, 50(9), 1-32, doi:10.18637/jss.v050.i09

See Also

NmgPVPS, fPump, pumpCoef

Description

Compute the irradiance and irradiation including shadows for two-axis and horizontal N-S axis trackers and fixed surfaces. It makes use of the function fSombra for the shadows factor calculation. It is used by the function calcGef.

Usage

calcShd(radEf, modeTrk = 'fixed', modeShd = '',
        struct = list(),
        distances = data.frame())

Arguments

Value

A Gef object including three additional variables (Gef0, Def0 and Bef0) in the slots GefI, GefD, Gefdm and Gefy with the irradiance/irradiation without shadows as a reference.

Author(s)

Oscar Perpiñán Lamigueiro.

References

• Perpiñán, O, Energía Solar Fotovoltaica, 2015. (https://oscarperpinan.github.io/esf/)

• Perpiñán, O. (2012), "solaR: Solar Radiation and Photovoltaic Systems with R", Journal of Statistical Software, 50(9), 1-32, doi:10.18637/jss.v050.i09

See Also

calcG0, fTheta, fInclin, calcShd.

Description

The optimum distance between trackers or static structures of a PV grid connected plant depends on two main factors: the ground requirement ratio (defined as the ratio of the total ground area to the generator PV array area), and the productivity of the system including shadow losses. Therefore, the optimum separation may be the one which achieves the highest productivity with the lowest ground requirement ratio.

However, this definition is not complete since the terrain characteristics and the costs of wiring or civil works could alter the decision. This function is a help for choosing this distance: it computes the productivity for a set of combinations of distances between the elements of the plant.

Usage

optimShd(lat,
         modeTrk = 'fixed',
         modeRad = 'prom',
         dataRad,
         sample = 'hour',
         keep.night = TRUE,
         sunGeometry = 'michalsky',
         betaLim = 90, beta = abs(lat)-10, alfa = 0,
         iS = 2, alb = 0.2, HCPV = FALSE,
         module = list(),
         generator = list(),
         inverter = list(),
         effSys = list(),
         modeShd = '',
         struct = list(),
         distances = data.frame(),
         res = 2,
         prog = TRUE)

Arguments

Details

optimShd calculates the energy produced for every combination of distances as defined by distances and res. The result of this function is a Shade-class object. A method of shadeplot for this class is defined (shadeplot-methods), and it shows the graphical relation between the productivity and the distance between trackers or fixed surfaces.

Value

A Shade object.

Author(s)

Oscar Perpiñán Lamigueiro

References

• Perpiñán, O.: Grandes Centrales Fotovoltaicas: producción, seguimiento y ciclo de vida. PhD Thesis, UNED, 2008. http://e-spacio.uned.es/fez/eserv/tesisuned:IngInd-Operpinan/GrandesCentrales.pdf.

• Perpiñán, O, Energía Solar Fotovoltaica, 2015. (https://oscarperpinan.github.io/esf/)

• Perpiñán, O. (2012), "solaR: Solar Radiation and Photovoltaic Systems with R", Journal of Statistical Software, 50(9), 1-32, doi:10.18637/jss.v050.i09

See Also

prodGCPV, calcShd

Examples

library(lattice)
library(latticeExtra)

lat = 37.2;
G0dm = c(2766, 3491, 4494, 5912, 6989, 7742, 7919, 7027, 5369, 3562, 2814,
2179)
Ta = c(10, 14.1, 15.6, 17.2, 19.3, 21.2, 28.4, 29.9, 24.3, 18.2, 17.2, 15.2)
prom = list(G0dm = G0dm, Ta = Ta)

###Two-axis trackers
struct2x = list(W = 23.11, L = 9.8, Nrow = 2, Ncol = 3)
dist2x = list(Lew = c(30, 45),Lns = c(20, 40))

ShdM2x <- optimShd(lat = lat, dataRad = prom, modeTrk = 'two',
                   modeShd = c('area','prom'),
                   distances = dist2x, struct = struct2x,
                   res = 5)

shadeplot(ShdM2x)

pLew = xyplot(Yf~GRR,data = ShdM2x,groups = factor(Lew),type = c('l','g'),
    main = 'Productivity for each Lew value')
pLew+glayer(panel.text(x[1], y[1], group.value))

pLns = xyplot(Yf~GRR,data = ShdM2x,groups = factor(Lns),type = c('l','g'),
    main = 'Productivity for each Lns value')
pLns+glayer(panel.text(x[1], y[1], group.value))

## 1-axis tracker with Backtracking
structHoriz = list(L = 4.83);
distHoriz = list(Lew = structHoriz$L * c(2,5));


Shd12HorizBT <- optimShd(lat = lat, dataRad = prom,
        modeTrk = 'horiz',
        betaLim = 60,
        distances = distHoriz, res = 2,
        struct = structHoriz,
        modeShd = 'bt')

shadeplot(Shd12HorizBT)

xyplot(diff(Yf)~GRR[-1],data = Shd12HorizBT,type = c('l','g'))

###Fixed system
structFixed = list(L = 5);
distFixed = list(D = structFixed$L*c(1,3));
Shd12Fixed <- optimShd(lat = lat, dataRad = prom,
        modeTrk = 'fixed',
        distances = distFixed, res = 2,
        struct = structFixed,
        modeShd = 'area')
shadeplot(Shd12Fixed)

Description

Constructor for the class Meteo with values of daily or intradaily values of global horizontal irradiation and ambient temperature from a local file or a data.frame.

Usage

readBD(file,  lat,
       format = '%d/%m/%Y',
       header = TRUE, fill = TRUE, dec = '.', sep = ';',
       dates.col = 'date',source = file)

readBDi(file,  lat,
       format = '%d/%m/%Y %H:%M:%S',
       header = TRUE, fill = TRUE, dec = '.', sep = ';',
       time.col = 'time',
       source = file)

df2Meteo(file,  lat,
         format = '%d/%m/%Y',
         dates.col = 'date',
         source = '')

dfI2Meteo(file,  lat,
         format = '%d/%m/%Y %H:%M:%S',
         time.col = 'time',
         source = '')

zoo2Meteo(file, lat, source = '')

Arguments

Value

A Meteo object.

Author(s)

Oscar Perpiñán Lamigueiro.

See Also

read.table, readG0dm.

Examples

data(helios)
names(helios) = c('date', 'G0', 'TempMax', 'TempMin')

bd = df2Meteo(helios, dates.col = 'date', lat = 41, source = 'helios-IES', format = '%Y/%m/%d')

summary(getData(bd))

xyplot(bd)

Description

Constructor for the class Meteo with 12 values of monthly means of irradiation.

Usage

readG0dm(G0dm, Ta = 25, lat = 0,
    year= as.POSIXlt(Sys.Date())$year+1900,
    promDays = c(17,14,15,15,15,10,18,18,18,19,18,13),
    source = '')

Arguments

Value

Meteo object

Author(s)

Oscar Perpiñán Lamigueiro.

See Also

readBD

Examples

G0dm =
  c(2.766,3.491,4.494,5.912,6.989,7.742,7.919,7.027,5.369,3.562,2.814,2.179) * 1000;
Ta = c(10, 14.1, 15.6, 17.2, 19.3, 21.2, 28.4, 29.9, 24.3, 18.2, 17.2, 15.2)
BD <- readG0dm(G0dm = G0dm, Ta = Ta, lat = 37.2)
print(BD)
getData(BD)
xyplot(BD)

Description

A class for meteorological data.

Objects from the Class

Objects can be created by the family of readBD functions.

Slots

latData:

Latitude (degrees) of the meteorological station or source of the data.

data:

A zoo object with the time series of daily irradiation (G0, Wh/m²), the ambient temperature (Ta) or the maximum and minimum ambient temperature (TempMax and TempMin).

source:

A character with a short description of the source of the data.

type:

A character, prom, bd, bdI or mapa, depending on the constructor.

Methods

getData

signature(object = "Meteo"): extracts the data slot as a zoo object.

getG0

signature(object = "Meteo"): extracts the irradiation time series as a zoo object.

getLat

signature(object = "Meteo"): extracts the latitude value.

indexD

signature(object = "Meteo"): extracts the index of the data slot.

xyplot

signature(x = "formula", data = "Meteo"): plot the content of the object according to the formula argument.

xyplot

signature(x = "Meteo", data = "missing"): plot the data slot using the xyplot method for zoo objects.

Author(s)

Oscar Perpiñán Lamigueiro.

See Also

readBD, readBDi, zoo2Meteo, df2Meteo, dfI2Meteo, readG0dm,

Description

A class which describe the apparent movement of the Sun from the Earth.

Objects from the Class

Objects can be created by calcSol.

Slots

lat:

numeric, latitude (degrees) as defined in the call to calcSol.

solD:

Object of class "zoo" created by fSolD.

solI:

Object of class "zoo" created by fSolI.

match:

numeric, index of solD related with the index of solI.

method:

character, method for the sun geometry calculations.

sample:

difftime, increment of the intradaily sequence.

Methods

as.data.frameD

signature(object = "Sol"): conversion to a data.frame with daily values.

as.data.frameI

signature(object = "Sol"): conversion to a data.frame with intradaily values.

as.zooD

signature(object = "Sol"): conversion to a zoo object with daily values.

as.zooI

signature(object = "Sol"): conversion to a zoo object with intradaily values.

getLat

signature(object = "Sol"): latitude (degrees) as defined in the call to calcSol.

indexD

signature(object = "Sol"): index of the solD slot.

indexI

signature(object = "Sol"): index of the solI object.

indexRep

signature(object = "Sol"): accesor for the match slot.

xyplot

signature(x = "formula", data = "Sol"): displays the contents of a Sol object with the xyplot method for formulas.

Author(s)

Oscar Perpiñán Lamigueiro.

References

• Perpiñán, O, Energía Solar Fotovoltaica, 2015. (https://oscarperpinan.github.io/esf/)

• Perpiñán, O. (2012), "solaR: Solar Radiation and Photovoltaic Systems with R", Journal of Statistical Software, 50(9), 1-32, doi:10.18637/jss.v050.i09

See Also

G0, Gef.

Description

This class contains the global, diffuse and direct irradiation and irradiance on the horizontal plane, and ambient temperature.

Objects from the Class

Objects can be created by the function calcG0.

Slots

G0D:

Object of class "zoo" created by fCompD. It includes daily values of:

Fd:

numeric, the diffuse fraction

Ktd:

numeric, the clearness index

G0d:

numeric, the global irradiation on a horizontal surface (Wh/m²)

D0d:

numeric, the diffuse irradiation on a horizontal surface (Wh/m²)

B0d:

numeric, the direct irradiation on a horizontal surface (Wh/m²)

G0I:

Object of class "zoo" created by fCompI. It includes values of:

kt:

numeric, clearness index

G0:

numeric, global irradiance on a horizontal surface, (W/m²)

D0:

numeric, diffuse irradiance on a horizontal surface, (W/m²)

B0:

numeric, direct irradiance on a horizontal surface, (W/m²)

G0dm:

Object of class "zoo" with monthly mean values of daily irradiation.

G0y:

Object of class "zoo" with yearly sums of irradiation.

Ta:

Object of class "zoo" with intradaily ambient temperature values.

Besides, this class contains the slots from the Sol and Meteo classes.

Extends

Class "Meteo", directly. Class "Sol", directly.

Methods

as.zooD

signature(object = "G0"): conversion to a zoo object with daily values.

as.zooI

signature(object = "G0"): conversion to a zoo object with intradaily values.

as.zooM

signature(object = "G0"): conversion to a zoo object with monthly values.

as.zooY

signature(object = "G0"): conversion to a zoo object with yearly values.

as.data.frameD

signature(object = "G0"): conversion to a data.frame with daily values.

as.data.frameI

signature(object = "G0"): conversion to a data.frame with intradaily values.

as.data.frameM

signature(object = "G0"): conversion to a data.frame with monthly values.

as.data.frameY

signature(object = "G0"): conversion to a data.frame with yearly values.

indexD

signature(object = "G0"): index of the solD slot.

indexI

signature(object = "G0"): index of the solI object.

indexRep

signature(object = "G0"): accesor for the match slot.

getLat

signature(object = "G0"): latitude of the inherited Sol object.

xyplot

signature(x = "G0", data = "missing"): display the time series of daily values of irradiation.

xyplot

signature(x = "formula", data = "G0"): displays the contents of a G0 object with the xyplot method for formulas.

Author(s)

Oscar Perpiñán Lamigueiro.

References

• Perpiñán, O, Energía Solar Fotovoltaica, 2015. (https://oscarperpinan.github.io/esf/)

• Perpiñán, O. (2012), "solaR: Solar Radiation and Photovoltaic Systems with R", Journal of Statistical Software, 50(9), 1-32, doi:10.18637/jss.v050.i09

See Also

Sol, Gef.

Description

This class contains the global, diffuse and direct irradiation and irradiance on the horizontal plane, and ambient temperature.

Objects from the Class

Objects can be created by the function calcGef.

Slots

GefI:

Object of class "zoo" created by fInclin. It contains these components:

Bo:

Extra-atmospheric irradiance on the inclined surface (W/m²)

Bn:

Direct normal irradiance (W/m²)

G, B, D, Di, Dc, R:

Global, direct, diffuse (total, isotropic and anisotropic) and albedo irradiance incident on an inclined surface (W/m²)

Gef, Bef, Def, Dief, Dcef, Ref:

Effective global, direct, diffuse (total, isotropic and anisotropic) and albedo irradiance incident on an inclined surface (W/m²)

FTb, FTd, FTr:

Factor of angular losses for the direct, diffuse and albedo components

GefD:

Object of class "zoo" with daily values of global, diffuse and direct irradiation.

Gefdm:

Object of class "zoo" with monthly means of daily global, diffuse and direct irradiation.

Gefy:

Object of class "zoo" with yearly sums of global, diffuse and direct irradiation.

Theta:

Object of class "zoo" created by fTheta. It contains these components:

Beta:

numeric, inclination angle of the surface (radians). When modeTrk='fixed' it is the value of the argument beta converted from degreesto radians.

Alfa:

numeric, azimuth angle of the surface (radians). When modeTrk='fixed' it is the value of the argument alfa converted from degrees to radians.

cosTheta:

numeric, cosine of the incidence angle of the solar irradiance on the surface

iS:

numeric, degree of dirtiness.

alb:

numeric, albedo reflection coefficient.

modeTrk:

character, mode of tracking.

modeShd:

character, mode of shadows.

angGen:

A list with the values of alfa, beta and betaLim.

struct:

A list with the dimensions of the structure.

distances:

A data.frame with the distances between structures.

Extends

Class "G0", directly. Class "Meteo", by class "G0", distance 2. Class "Sol", by class "G0", distance 2.

Methods

as.zooD

signature(object = "Gef"): conversion to a zoo object with daily values.

as.zooI

signature(object = "Gef"): conversion to a zoo object with intradaily values.

as.zooM

signature(object = "Gef"): conversion to a zoo object with monthly values.

as.zooY

signature(object = "Gef"): conversion to a zoo object with yearly values.

as.data.frameD

signature(object = "Gef"): conversion to a data.frame with daily values.

as.data.frameI

signature(object = "Gef"): conversion to a data.frame with intradaily values.

as.data.frameM

signature(object = "Gef"): conversion to a data.frame with monthly values.

as.data.frameY

signature(object = "Gef"): conversion to a data.frame with yearly values.

indexD

signature(object = "Gef"): index of the solD slot.

indexI

signature(object = "Gef"): index of the solI object.

indexRep

signature(object = "Gef"): accesor for the match slot.

getLat

signature(object = "Gef"): latitude of the inherited Sol object.

xyplot

signature(x = "Gef", data = "missing"): display the time series of daily values of irradiation.

xyplot

signature(x = "formula", data = "Gef"): displays the contents of a Gef object with the xyplot method for formulas.

Author(s)

Oscar Perpiñán Lamigueiro.

References

• Perpiñán, O, Energía Solar Fotovoltaica, 2015. (https://oscarperpinan.github.io/esf/)

• Perpiñán, O. (2012), "solaR: Solar Radiation and Photovoltaic Systems with R", Journal of Statistical Software, 50(9), 1-32, doi:10.18637/jss.v050.i09

See Also

Sol, G0.

Description

A class containing values of the performance of a grid connected PV system.

Objects from the Class

Objects can be created by prodGCPV.

Slots

prodI:

Object of class "zoo" created by fProd. It includes these components:

Tc:

cell temperature, $^{\circ}{\rm C}$.

Voc, Isc, Vmpp, Impp:

open circuit voltage, short circuit current, MPP voltage and current, respectively.

Vdc, Idc:

voltage and current at the input of the inverter.

Pdc:

power at the input of the inverter, W

Pac:

power at the output of the inverter, W

EffI:

efficiency of the inverter

prodD:

A zoo object with daily values of AC (Eac) and DC (Edc) energy (Wh), and productivity (Yf, Wh/Wp) of the system.

prodDm:

A zoo object with monthly means of daily values of AC and DC energy (kWh), and productivity of the system.

prody:

A zoo object with yearly sums of AC and DC energy (kWh), and productivity of the system.

module:

A list with the characteristics of the module.

generator:

A list with the characteristics of the PV generator.

inverter:

A list with the characteristics of the inverter.

effSys:

A list with the efficiency values of the system.

Besides, this class contains the slots from the "Meteo", "Sol", "G0" and "Gef" classes.

Extends

Class "Gef", directly. Class "G0", by class "Gef", distance 2. Class "Meteo", by class "Gef", distance 3. Class "Sol", by class "Gef", distance 3.

Methods

as.zooD

signature(object = "ProdGCPV"): conversion to a zoo object with daily values.

as.zooI

signature(object = "ProdGCPV"): conversion to a zoo object with intradaily values.

as.zooM

signature(object = "ProdGCPV"): conversion to a zoo object with monthly values.

as.zooY

signature(object = "ProdGCPV"): conversion to a zoo object with yearly values.

as.data.frameD

signature(object = "ProdGCPV"): conversion to a data.frame with daily values.

as.data.frameI

signature(object = "ProdGCPV"): conversion to a data.frame with intradaily values.

as.data.frameM

signature(object = "ProdGCPV"): conversion to a data.frame with monthly values.

as.data.frameY

signature(object = "ProdGCPV"): conversion to a data.frame with yearly values.

indexD

signature(object = "ProdGCPV"): index of the solD slot.

indexI

signature(object = "ProdGCPV"): index of the solI object.

indexRep

signature(object = "ProdGCPV"): accesor for the match slot.

getLat

signature(object = "ProdGCPV"): latitude of the inherited Sol object.

xyplot

signature(x = "ProdGCPV", data = "missing"): display the time series of daily values.

xyplot

signature(x = "formula", data = "ProdGCPV"): displays the contents of a ProdGCPV object with the xyplot method for formulas.

as.zooD

signature(object = "ProdGCPV"): conversion to a zoo object with daily values.

as.zooI

signature(object = "ProdGCPV"): conversion to a zoo object with intradaily values.

Author(s)

Oscar Perpiñán Lamigueiro.

References

• Perpiñán, O, Energía Solar Fotovoltaica, 2015. (https://oscarperpinan.github.io/esf/)

• Perpiñán, O. (2012), "solaR: Solar Radiation and Photovoltaic Systems with R", Journal of Statistical Software, 50(9), 1-32, doi:10.18637/jss.v050.i09

See Also

Sol, G0, Gef, Shade.

Description

Performance of a PV pumping system with a centrifugal pump and a variable frequency converter.

Objects from the Class

Objects can be created by prodPVPS.

Slots

prodI:

Object of class "zoo" with these components:

Q:

Flow rate, (m³/h)

Pb, Ph:

Pump shaft power and hydraulical power (W), respectively.

etam, etab:

Motor and pump efficiency, respectively.

f:

Frequency (Hz)

prodD:

A zoo object with daily values of AC energy (Wh), flow (m³) and productivity of the system.

prodDm:

A zoo object with monthly means of daily values of AC energy (kWh), flow (m³) and productivity of the system.

prody:

A zoo object with yearly sums of AC energy (kWh), flow (m³) and productivity of the system.

pump

A list extracted from pumpCoef

H

Total manometric head (m)

Pg

Nominal power of the PV generator (Wp)

converter

list containing the nominal power of the frequency converter, Pnom, and Ki, vector of three values, coefficients of the efficiency curve.

effSys

list of numeric values with information about the system losses

Besides, this class contains the slots from the Gef class.

Extends

Class "Gef", directly. Class "G0", by class "Gef", distance 2. Class "Meteo", by class "Gef", distance 3. Class "Sol", by class "Gef", distance 3.

Methods

as.zooD

signature(object = "ProdPVPS"): conversion to a zoo object with daily values.

as.zooI

signature(object = "ProdPVPS"): conversion to a zoo object with intradaily values.

as.zooM

signature(object = "ProdPVPS"): conversion to a zoo object with monthly values.

as.zooY

signature(object = "ProdPVPS"): conversion to a zoo object with yearly values.

as.data.frameD

signature(object = "ProdPVPS"): conversion to a data.frame with daily values.

as.data.frameI

signature(object = "ProdPVPS"): conversion to a data.frame with intradaily values.

as.data.frameM

signature(object = "ProdPVPS"): conversion to a data.frame with monthly values.

as.data.frameY

signature(object = "ProdPVPS"): conversion to a data.frame with yearly values.

indexD

signature(object = "ProdPVPS"): index of the solD slot.

indexI

signature(object = "ProdPVPS"): index of the solI object.

indexRep

signature(object = "ProdPVPS"): accesor for the match slot.

getLat

signature(object = "ProdPVPS"): latitude of the inherited Sol object.

xyplot

signature(x = "ProdPVPS", data = "missing"): display the time series of daily values.

xyplot

signature(x = "formula", data = "ProdPVPS"): displays the contents of a ProdPVPS object with the xyplot method for formulas.

Author(s)

Oscar Perpiñán Lamigueiro.

References

• Abella, M. A., Lorenzo, E. y Chenlo, F.: PV water pumping systems based on standard frequency converters. Progress in Photovoltaics: Research and Applications, 11(3):179–191, 2003, ISSN 1099-159X.

• Perpiñán, O, Energía Solar Fotovoltaica, 2015. (https://oscarperpinan.github.io/esf/)

• Perpiñán, O. (2012), "solaR: Solar Radiation and Photovoltaic Systems with R", Journal of Statistical Software, 50(9), 1-32, doi:10.18637/jss.v050.i09

See Also

prodPVPS, fPump.

Description

A class for the optimization of shadows in a PV system.

Objects from the Class

Objects can be created by optimShd.

Slots

FS:

numeric, shadows factor values for each combination of distances.

GRR:

numeric, Ground Requirement Ratio for each combination.

Yf:

numeric, final productivity for each combination.

FS.loess:

A local fitting of FS with loess.

Yf.loess:

A local fitting of Yf with loess.

modeShd:

character, mode of shadows.

struct:

A list with the dimensions of the structure.

distances:

A data.frame with the distances between structures.

res

numeric, difference (meters) between the different steps of the calculation.

Besides, as a reference, this class includes a ProdGCPV object with the performance of a PV systems without shadows.

Extends

Class "ProdGCPV", directly. Class "Gef", by class "ProdGCPV", distance 2. Class "G0", by class "ProdGCPV", distance 3. Class "Meteo", by class "ProdGCPV", distance 4. Class "Sol", by class "ProdGCPV", distance 4.

Methods

as.data.frame

signature(x = "Shade"): conversion to a data.frame including columns for distances (Lew, Lns, and D) and results (FS, GRR and Yf).

shadeplot

signature(x = "Shade"): display the results of the iteration with a level plot for the two-axis tracking, or with conventional plot for horizontal tracking and fixed systems.

xyplot

signature(x = "formula", data = "Shade"): display the content of the Shade object with the xyplot method for formulas.

Author(s)

Oscar Perpiñán Lamigueiro.

References

• Perpiñán, O.: Grandes Centrales Fotovoltaicas: producción, seguimiento y ciclo de vida. PhD Thesis, UNED, 2008. http://e-spacio.uned.es/fez/eserv/tesisuned:IngInd-Operpinan/GrandesCentrales.pdf.

• Perpiñán, O, Energía Solar Fotovoltaica, 2015. (https://oscarperpinan.github.io/esf/)

• Perpiñán, O. (2012), "solaR: Solar Radiation and Photovoltaic Systems with R", Journal of Statistical Software, 50(9), 1-32, doi:10.18637/jss.v050.i09

See Also

Gef, ProdGCPV.

Description

A set of correlations between the fraction of diffuse irradiation and the clearness index used by fCompD and fCompI.

Usage

## Monthly means of daily values
FdKtPage(Ktd)
FdKtLJ(Ktd)

## Daily values
FdKtCPR(Ktd)
FdKtEKDd(Ktd, sol)
FdKtCLIMEDd(Ktd)

## Intradaily values
FdKtEKDh(kt)
FdKtCLIMEDh(kt)
FdKtBRL(kt, sol)

Arguments

Value

A numeric, the diffuse fraction.

Author(s)

Oscar Perpiñán Lamigueiro; The BRL model was suggested by Kevin Ummel.

References

• Page, J. K., The calculation of monthly mean solar radiation for horizontal and inclined surfaces from sunshine records for latitudes 40N-40S. En U.N. Conference on New Sources of Energy, vol. 4, págs. 378–390, 1961.

• Collares-Pereira, M. y Rabl, A., The average distribution of solar radiation: correlations between diffuse and hemispherical and between daily and hourly insolation values. Solar Energy, 22:155–164, 1979.

• Erbs, D.G, Klein, S.A. and Duffie, J.A., Estimation of the diffuse radiation fraction for hourly, daily and monthly-average global radiation. Solar Energy, 28:293:302, 1982.

• De Miguel, A. et al., Diffuse solar irradiation model evaluation in the north mediterranean belt area, Solar Energy, 70:143-153, 2001.

• Ridley, B., Boland, J. and Lauret, P., Modelling of diffuse solar fraction with multiple predictors, Renewable Energy, 35:478-482, 2010.

See Also

fCompD, fCompI

Examples

Ktd = seq(0, 1, .01)
Monthly = data.frame(Ktd = Ktd)
Monthly$Page = FdKtPage(Ktd)
Monthly$LJ = FdKtLJ(Ktd)

xyplot(Page+LJ~Ktd, data = Monthly,
       type = c('l', 'g'), auto.key = list(space = 'right'))

Ktd = seq(0, 1, .01)
Daily = data.frame(Ktd = Ktd)
Daily$CPR = FdKtCPR(Ktd)
Daily$CLIMEDd = FdKtCLIMEDd(Ktd)

xyplot(CPR + CLIMEDd ~ Ktd, data = Daily,
       type = c('l', 'g'), auto.key = list(space = 'right'))

Description

Construction of a daily time base for solar irradiation calculation

Usage

fBTd(mode = "prom",
    year = as.POSIXlt(Sys.Date())$year+1900,
    start = paste('01-01-',year,sep = ''),
    end = paste('31-12-',year,sep = ''),
                   format = '%d-%m-%Y')

Arguments

Details

This function is commonly used inside fSolD.

Value

This function returns a POSIXct object.

Author(s)

Oscar Perpiñán Lamigueiro

References

• Perpiñán, O, Energía Solar Fotovoltaica, 2015. (https://oscarperpinan.github.io/esf/)

• Perpiñán, O. (2012), "solaR: Solar Radiation and Photovoltaic Systems with R", Journal of Statistical Software, 50(9), 1-32, doi:10.18637/jss.v050.i09

See Also

fSolD, as.POSIXct, seq.POSIXt.

Examples

#Average days
fBTd(mode = 'prom')

#The day #100 of the year 2008
BTd = fBTd(mode = 'serie', year = 2008)
BTd[100]

Description

Extract the diffuse and direct components from the daily global irradiation on a horizontal surface by means of regressions between the clearness index and the diffuse fraction parameters.

Usage

fCompD(sol, G0d, corr = "CPR",f)

Arguments

Value

A zoo object which includes:

Author(s)

Oscar Perpiñán Lamigueiro

References

• Perpiñán, O, Energía Solar Fotovoltaica, 2015. (https://oscarperpinan.github.io/esf/)

• Perpiñán, O. (2012), "solaR: Solar Radiation and Photovoltaic Systems with R", Journal of Statistical Software, 50(9), 1-32, doi:10.18637/jss.v050.i09

See Also

fCompI

Examples

lat = 37.2;
BTd = fBTd(mode = 'serie')

SolD <- fSolD(lat, BTd[100])

G0d = zoo(5000, index(SolD))
fCompD(SolD, G0d, corr = "Page")
fCompD(SolD, G0d, corr = "CPR")

#define a function fKtd with the correlation of CPR
fKTd = function(x){(0.99*(x <= 0.17))+
                 (x>0.17)*(1.188 -2.272 * x + 9.473 * x^2 - 21.856 * x^3
+ 14.648 * x^4)}
#The same as with corr = "CPR"
fCompD(SolD, G0d, corr = "user", f = fKTd)

lat = -37.2;
SolDs <- fSolD(lat, BTd[283])
G0d = zoo(5000, index(SolDs))
fCompD(SolDs, G0d, corr = "CPR")

lat = 37.2;
G0dm = c(2.766,3.491,4.494,5.912,6.989,7.742,7.919,7.027,5.369,3.562,2.814,2.179)*1000;
Rad = readG0dm(G0dm, lat = lat)
solD <- fSolD(lat,fBTd(mode = 'prom'))
fCompD(solD, Rad, corr = 'Page')

Description

From the daily global, diffuse and direct irradiation values supplied by fCompD, the profile of the global, diffuse and direct irradiance is calculated with the rd and rg components of fSolI.

Usage

fCompI(sol, compD, G0I, corr = 'none', f, filterG0 = TRUE)

Arguments

Value

A zoo with these components:

Author(s)

Oscar Perpiñán Lamigueiro.

References

• Collares-Pereira, M. y Rabl, A., The average distribution of solar radiation: correlations between diffuse and hemispherical and between daily and hourly insolation values. Solar Energy, 22:155–164, 1979.

• Perpiñán, O, Energía Solar Fotovoltaica, 2015. (https://oscarperpinan.github.io/esf/)

• Perpiñán, O. (2012), "solaR: Solar Radiation and Photovoltaic Systems with R", Journal of Statistical Software, 50(9), 1-32, doi:10.18637/jss.v050.i09

See Also

fCompD, fSolI, calcSol, corrFdKt.

Examples

lat <- 37.2

BTd <- fBTd(mode = 'serie')
solD <- fSolD(lat, BTd[100])
solI <- fSolI(solD, sample = 'hour')
G0d <- zoo(5000, index(solD))
compD <- fCompD(solD, G0d, corr = "Page")
fCompI(solI, compD)

sol <- calcSol(lat, fBTd(mode = 'prom'), sample = 'hour', keep.night = FALSE)

G0dm <- c(2.766, 3.491, 4.494, 5.912, 6.989, 7.742,
          7.919, 7.027, 5.369, 3.562, 2.814, 2.179)*1000

Ta <- c(10, 14.1, 15.6, 17.2, 19.3, 21.2, 28.4, 29.9,
        24.3, 18.2, 17.2, 15.2)

BD <- readG0dm(G0dm = G0dm, Ta = Ta, lat = lat)
compD <- fCompD(sol, BD, corr = 'Page')
compI <- fCompI(sol, compD)
head(compI)

## Use of 'corr'.  The help page of calcG0 includes additional examples
## with intradaily data xyplot(fd ~ kt, data = compI)

climed <- fCompI(sol, G0I = compI, corr = 'CLIMEDh')
xyplot(fd ~ kt, data = climed)

ekdh <- fCompI(sol, G0I = compI, corr = 'EKDh')
xyplot(fd ~ kt, data = ekdh)

brl <- fCompI(sol, G0I = compI, corr = 'BRL')
xyplot(fd ~ kt, data = brl)

Description

The solar irradiance incident on an inclined surface is calculated from the direct and diffuse irradiance on a horizontal surface, and from the evolution of the angles of the Sun and the surface. Moreover, the effect of the angle of incidence and dust on the PV module is included to obtain the effective irradiance.

This function is used by the calcGef function.

Usage

fInclin(compI, angGen, iS = 2, alb = 0.2, horizBright = TRUE, HCPV = FALSE)

Arguments

Details

The solar irradiance incident on an inclined surface can be calculated from the direct and diffuse irradiance on a horizontal surface, and from the evolution of the angles of the Sun and the surface. The transformation of the direct radiation is straightforward since only geometric considerations are needed. However, the treatment of the diffuse irradiance is more complex since it involves the modelling of the atmosphere. There are several models for the estimation of diffuse irradiance on an inclined surface. The one which combines simplicity and acceptable results is the proposal of Hay and McKay. This model divides the diffuse component in isotropic and anisotropic whose values depends on a anisotropy index. On the other hand, the effective irradiance, the fraction of the incident irradiance that reaches the cells inside a PV module, is calculated with the losses due to the angle of incidence and dirtiness. This behaviour can be simulated with a model proposed by Martin and Ruiz requiring information about the angles of the surface and the level of dirtiness (iS) .

Value

A zoo object with these components:

Author(s)

Oscar Perpiñán Lamigueiro.

References

• Hay, J. E. and McKay, D. C.: Estimating Solar Irradiance on Inclined Surfaces: A Review and Assessment of Methodologies. Int. J. Solar Energy, (3):pp. 203, 1985.

• Martin, N. and Ruiz, J.M.: Calculation of the PV modules angular losses under field conditions by means of an analytical model. Solar Energy Materials & Solar Cells, 70:25–38, 2001.

• D. T. Reindl and W. A. Beckman and J. A. Duffie: Evaluation of hourly tilted surface radiation models, Solar Energy, 45:9-17, 1990.

• Perpiñán, O, Energía Solar Fotovoltaica, 2015. (https://oscarperpinan.github.io/esf/)

• Perpiñán, O. (2012), "solaR: Solar Radiation and Photovoltaic Systems with R", Journal of Statistical Software, 50(9), 1-32, doi:10.18637/jss.v050.i09

See Also

fTheta, fCompI, calcGef.

Description

Simulate the behaviour of a grid connected PV system under different conditions of irradiance and temperature. This function is used by the prodGCPV function.

Usage

fProd(inclin, module, generator, inverter, effSys)

Arguments

Value

If inclin is zoo or Gef object, the result is a zoo object with these components (if inclin is a data.frame the result is also a data.frame with these same components):

Author(s)

Oscar Perpiñán Lamigueiro

References

• Jantsch, M., Schmidt, H. y Schmid, J.: Results on the concerted action on power conditioning and control. 11th European photovoltaic Solar Energy Conference, 1992.

• Baumgartner, F. P., Schmidt, H., Burger, B., Bründlinger, R., Haeberlin, H. and Zehner, M.: Status and Relevance of the DC Voltage Dependency of the Inverter Efficiency. 22nd European Photovoltaic Solar Energy Conference, 2007.

• Alonso Garcia, M. C.: Caracterización y modelado de asociaciones de dispositivos fotovoltaicos. PhD Thesis, CIEMAT, 2005.

• Perpiñán, O, Energía Solar Fotovoltaica, 2015. (https://oscarperpinan.github.io/esf/)

• Perpiñán, O. (2012), "solaR: Solar Radiation and Photovoltaic Systems with R", Journal of Statistical Software, 50(9), 1-32, doi:10.18637/jss.v050.i09

See Also

fInclin, prodGCPV, fTemp.

Examples

inclin = data.frame(Gef = c(200,400,600,800,1000),Ta = 25)

#using default values
fProd(inclin)

#Using a matrix for Ki (voltage dependence)
inv1 <- list(Ki = rbind(c(-0.00019917, 7.513e-06, -5.4183e-09),
c(0.00806, -4.161e-06, 2.859e-08),
c(0.02118, 3.4002e-05, -4.8967e-08)))

fProd(inclin, inverter = inv1)

#Voltage limits of the inverter
inclin = data.frame(Gef = 800,Ta = 30)
gen1 = list(Nms = 10, Nmp = 11)

prod = fProd(inclin,generator = gen1)
print(prod)

with(prod, Vdc * Idc / (Vmpp * Impp))

Description

Compute the performance of the different parts of a centrifugal pump fed by a frequency converter following the affinity laws.

Usage

fPump(pump, H)

Arguments

Value

Author(s)

Oscar Perpiñán Lamigueiro.

References

• Abella, M. A., Lorenzo, E. y Chenlo, F.: PV water pumping systems based on standard frequency converters. Progress in Photovoltaics: Research and Applications, 11(3):179–191, 2003, ISSN 1099-159X.

• Perpiñán, O, Energía Solar Fotovoltaica, 2015. (https://oscarperpinan.github.io/esf/)

• Perpiñán, O. (2012), "solaR: Solar Radiation and Photovoltaic Systems with R", Journal of Statistical Software, 50(9), 1-32, doi:10.18637/jss.v050.i09

See Also

NmgPVPS, prodPVPS, pumpCoef, splinefun.

Examples

library(latticeExtra)

data(pumpCoef)
CoefSP8A44 <- subset(pumpCoef, Qn == 8 & stages == 44)

fSP8A44 <- fPump(pump = CoefSP8A44,H = 40)
SP8A44 = with(fSP8A44,{
                Pac = seq(lim[1],lim[2],by = 100)
                Pb = fPb(Pac)
                etam = Pb/Pac
                Ph = fPh(Pac)
                etab = Ph/Pb
                f = fFreq(Pac)
                Q = fQ(Pac)
                result = data.frame(Q,Pac,Pb,Ph,etam,etab,f)})

#Efficiency of the motor, pump and the motor-pump
SP8A44$etamb = with(SP8A44,etab*etam)
lab = c(expression(eta[motor]), expression(eta[pump]), expression(eta[mp]))
p <- xyplot(etam + etab + etamb ~ Pac,data = SP8A44,type = 'l', ylab = 'Efficiency')
p+glayer(panel.text(x[1], y[1], lab[group.number], pos = 3))

#Mechanical, hydraulic and electrical power
lab = c(expression(P[pump]), expression(P[hyd]))
p <- xyplot(Pb + Ph ~ Pac,data = SP8A44,type = 'l', ylab = 'Power (W)', xlab = 'AC Power (W)')
p+glayer(panel.text(x[length(x)], y[length(x)], lab[group.number], pos = 3))

#Flow and electrical power
xyplot(Q ~ Pac,data = SP8A44,type = 'l')

Description

Compute the daily apparent movement of the Sun from the Earth. This movement is mainly described (for the simulation of photovoltaic systems) by the declination angle, the sunrise angle and the daily extra-atmospheric irradiation.

Usage

fSolD(lat, BTd, method = 'michalsky')

Arguments

Value

A zoo object with these components:

Note

The latitude is stored as the attribute lat of the result, and thus it is accessible with attr(object, 'lat').

Author(s)

Oscar Perpiñán Lamigueiro.

References

• Cooper, P.I., Solar Energy, 12, 3 (1969). "The Absorption of Solar Radiation in Solar Stills"

• Spencer, Search 2 (5), 172, https://www.mail-archive.com/[email protected]/msg01050.html

• Strous: https://www.aa.quae.nl/en/reken/zonpositie.html

• Michalsky, J., 1988: The Astronomical Almanac's algorithm for approximate solar position (1950-2050), Solar Energy 40, 227-235

• Perpiñán, O, Energía Solar Fotovoltaica, 2015. (https://oscarperpinan.github.io/esf/)

• Perpiñán, O. (2012), "solaR: Solar Radiation and Photovoltaic Systems with R", Journal of Statistical Software, 50(9), 1-32, doi:10.18637/jss.v050.i09

Examples

BTd <- fBTd(mode = 'serie')

lat <- 37.2
fSolD(lat, BTd[100])
fSolD(lat, BTd[100], method = 'strous')
fSolD(lat, BTd[100], method = 'spencer')
fSolD(lat, BTd[100], method = 'cooper')

lat <- -37.2
fSolD(lat, BTd[283])

#Solar angles along the year
SolD <- fSolD(lat, BTd = fBTd())

library(lattice)
xyplot(SolD)

#Calculation of the daylength for several latitudes
library(latticeExtra)

Lats <- c(-60, -40, -20, 0, 20, 40, 60)
NomLats <- ifelse(Lats > 0, paste(Lats,'N', sep = ''),
                  paste(abs(Lats), 'S', sep = ''))
NomLats[Lats == 0] <- '0'

mat <- matrix(nrow = 365, ncol = length(Lats))
colnames(mat) <- NomLats
WsZ <- zoo(mat, fBTd(mode = 'serie'))

for (i in seq_along(Lats)){
    SolDaux <- fSolD(lat = Lats[i], BTd = fBTd(mode = 'serie'));
    WsZ[,i] <- r2h(2*abs(SolDaux$ws))}

p = xyplot(WsZ, superpose = TRUE,
        ylab = expression(omega[s] (h)), auto.key = FALSE)
plab <- p+glayer(panel.text(x[1], y[1], NomLats[group.number], pos = 2))
print(plab)

Description

Compute the angles which describe the intradaily apparent movement of the Sun from the Earth.

Usage

fSolI(solD, sample = 'hour', BTi, EoT = TRUE, keep.night = TRUE, method = 'michalsky')

Arguments

Value

A zoo object is returned with these components:

The latitude is stored as the attribute lat of this object.

Author(s)

Oscar Perpiñán Lamigueiro.

References

• Cooper, P.I., Solar Energy, 12, 3 (1969). "The Absorption of Solar Radiation in Solar Stills"

• Spencer, Search 2 (5), 172, https://www.mail-archive.com/[email protected]/msg01050.html

• Strous: https://www.aa.quae.nl/en/reken/zonpositie.html

• Michalsky, J., 1988: The Astronomical Almanac's algorithm for approximate solar position (1950-2050), Solar Energy 40, 227-235

• Collares-Pereira, M. y Rabl, A., The average distribution of solar radiation: correlations between diffuse and hemispherical and between daily and hourly insolation values. Solar Energy, 22:155–164, 1979.

• Perpiñán, O, Energía Solar Fotovoltaica, 2015. (https://oscarperpinan.github.io/esf/)

• Perpiñán, O. (2012), "solaR: Solar Radiation and Photovoltaic Systems with R", Journal of Statistical Software, 50(9), 1-32, doi:10.18637/jss.v050.i09

See Also

fSolD

Examples

###Angles for one day
BTd = fBTd(mode = 'serie')

#North hemisphere
lat = 37.2
solD <- fSolD(lat,BTd[100])
solI <- fSolI(solD, sample = 'hour')
print(solI)

#South hemisphere
lat = -37.2;
solDs <- fSolD(lat,BTd[283])
solIs <- fSolI(solDs, sample = 'hour')
print(solIs)

 ###Angles for the 12 average days
lat = 37.2;
solD <- fSolD(lat,BTd = fBTd(mode = 'prom'))
solI <- fSolI(solD, sample = '10 min', keep.night = FALSE)

library(lattice)
library(latticeExtra)

###Solar elevation angle vs. azimuth.
#This kind of graphics is useful for shadows calculations
mon = month.abb
p <- xyplot(r2d(AlS)~r2d(AzS),
    groups = month,
    data = solI, type = 'l', col = 'black',
    xlab = expression(psi[s]),ylab = expression(gamma[s]))

plab <- p + glayer({
  idx <- round(length(x)/2+1)
  panel.text(x[idx], y[idx], mon[group.value], pos = 3, offset = 0.2, cex = 0.8)})

print(plab)

Description

Compute the shadows factor for two-axis and horizontal N-S axis trackers and fixed surfaces.

Usage

fSombra(angGen, distances, struct, modeTrk = 'fixed',prom = TRUE)

fSombra6(angGen,distances,struct,prom = TRUE)

fSombra2X(angGen,distances,struct)

fSombraHoriz(angGen, distances,struct)

fSombraEst(angGen, distances,struct)

Arguments

Details

fSombra is only a wrapper for fSombra6 (two-axis trackers), fSombraEst (fixed systems) and fSombraHoriz (horizontal N-S axis trackers). Depending on the value of modeTrk the corresponding function is selected. fSombra6 calculates the shadows factor in a set of six two-axis trackers. If distances has only one row, this function constructs a symmetric grid around a tracker located at (0,0,0). These five trackers are located at (-Lew, Lns, H), (0, Lns, H), (Lew, Lns, H), (-Lew, 0, H) and (Lns, 0, H). It is possible to define a irregular grid around (0,0,0) including five rows in distances. When prom = TRUE the shadows factor for each of the six trackers is calculated. Then, according to the distribution of trackers in the plant defined by struct$Nrow and struct$Ncol, a weighted average of the shadows factors is the result. It is important to note that the distances are defined between axis for trackers and between similar points of the structure for fixed surfaces.

Value

data.frame including angGen and a variable named FS, which is the shadows factor. This factor is the ratio between the area of the generator affected by shadows and the total area. Therefore its value is 1 when the PV generator is completely shadowed.

Author(s)

Oscar Perpiñán Lamigueiro.

References

• Perpiñán, O.: Grandes Centrales Fotovoltaicas: producción, seguimiento y ciclo de vida. PhD Thesis, UNED, 2008. http://e-spacio.uned.es/fez/eserv/tesisuned:IngInd-Operpinan/GrandesCentrales.pdf.

• Perpiñán, O, Energía Solar Fotovoltaica, 2015. (https://oscarperpinan.github.io/esf/)

• Perpiñán, O. (2012), "solaR: Solar Radiation and Photovoltaic Systems with R", Journal of Statistical Software, 50(9), 1-32, doi:10.18637/jss.v050.i09

See Also

calcShd, optimShd, fTheta, calcSol

Examples

lat = 37.2;
sol <- calcSol(lat, fBTd(mode = 'prom'), sample = '10 min', keep.night = FALSE)
angGen <- fTheta(sol, beta = 35);
Angles = CBIND(as.zooI(sol), angGen)

###Two-axis tracker
#Symmetric grid
distances = data.frame(Lew = 40,Lns = 30,H = 0)
struct = list(W = 23.11, L = 9.8, Nrow = 2, Ncol = 8)

ShdFactor <- fSombra6(Angles, distances, struct, prom = FALSE)

Angles$FS = ShdFactor
xyplot(FS ~ w, groups = month, data = Angles,
    type = 'l',
    auto.key = list(space = 'right',
                    lines = TRUE,
                    points = FALSE))

#Symmetric grid defined with a five rows data.frame
distances = data.frame(Lew = c(-40,0,40,-40,40),
                       Lns = c(30,30,30,0,0),
                       H = 0)
ShdFactor2 <- fSombra6(Angles, distances, struct,prom = FALSE)

#of course, with the same result
identical(coredata(ShdFactor), coredata(ShdFactor2))

Description

From the maximum and minimum daily values of ambient temperature, its evolution its calculated through a combination of cosine functions (ESRA method)

Usage

fTemp(sol, BD)

Arguments

Details

The ESRA method estimates the dependence of the temperature on the time of the day (given as the local solar time) from only two inputs: minimum and maximum daily temperatures. It assumes that the temperature daily profile can be described using three piecewise cosine functions, dividing the day into three periods: from midnight to sunrise, from sunrise to the time of peak temperature (3 hours after midday), and to midnight.

Value

A zoo object with the profile of the ambient temperature.

Author(s)

Oscar Perpiñán Lamigueiro.

References

• Huld, T. , Suri, M., Dunlop, E. D., and Micale F., Estimating average daytime and daily temperature profiles within Europe, Environmental Modelling & Software 21 (2006) 1650-1661.

• Perpiñán, O, Energía Solar Fotovoltaica, 2015. (https://oscarperpinan.github.io/esf/)

• Perpiñán, O. (2012), "solaR: Solar Radiation and Photovoltaic Systems with R", Journal of Statistical Software, 50(9), 1-32, doi:10.18637/jss.v050.i09

See Also

calcSol, readBD.

Description

The orientation, azimuth and incidence angle are calculated from the results of fSolI or calcSoland from the information supplied by the arguments beta and alfa when the surface is fixed (modeTrk = 'fixed') or the movement equations when a tracking surface is chosen (modeTrk = 'horiz' or modeTrk = 'two'). Besides, the modified movement of a horizontal NS tracker due to the backtracking strategy is calculated if BT = TRUE with information about the tracker and the distance between the trackers included in the system.

This function is used by the calcGef function.

Usage

fTheta(sol, beta, alfa = 0, modeTrk = "fixed", betaLim = 90,
    BT = FALSE, struct, dist)

Arguments

Value

A zoo object with these components:

Author(s)

Oscar Perpiñán Lamigueiro.

References

• Perpiñán, O, Energía Solar Fotovoltaica, 2015. (https://oscarperpinan.github.io/esf/)

• Panico, D., Garvison, P., Wenger, H. J., Shugar, D., Backtracking: a novel strategy for tracking PV systems, Photovoltaic Specialists Conference, 668-673, 1991

• Perpiñán, O. (2012), "solaR: Solar Radiation and Photovoltaic Systems with R", Journal of Statistical Software, 50(9), 1-32, doi:10.18637/jss.v050.i09

See Also

fInclin, fSombra, calcGef.

Description

Compute and display the H-Q curves of a centrifugal pump fed working at several frequencies, and the iso-efficiency curve as a reference.

Usage

HQCurve(pump)

Arguments

Value

Author(s)

Oscar Perpiñán Lamigueiro.

References

• Abella, M. A., Lorenzo, E. y Chenlo, F.: PV water pumping systems based on standard frequency converters. Progress in Photovoltaics: Research and Applications, 11(3):179–191, 2003, ISSN 1099-159X.

• Perpiñán, O, Energía Solar Fotovoltaica, 2015. (https://oscarperpinan.github.io/esf/)

• Perpiñán, O. (2012), "solaR: Solar Radiation and Photovoltaic Systems with R", Journal of Statistical Software, 50(9), 1-32, doi:10.18637/jss.v050.i09

See Also

NmgPVPS, prodPVPS, pumpCoef.

Examples

library(lattice)
library(latticeExtra)

data(pumpCoef)

CoefSP8A44 <- subset(pumpCoef, Qn == 8&stages == 44)
CurvaSP8A44 <- HQCurve(pump = CoefSP8A44)

Description

The function local2Solar converts the time zone of a POSIXct object to the mean solar time and set its time zone to UTC as a synonym of mean solar time. It includes two corrections: the difference of longitudes between the location and the time zone, and the daylight saving time.

The function CBIND combines several objects (zoo, data.frame or matrix) preserving the index of the first of them or asigning a new one with the index argument.

The function lonHH calculates the longitude (radians) of a time zone.

Usage

local2Solar(x, lon = NULL)
CBIND(..., index = NULL)
lonHH(tz)

Arguments

Details

Since the result of local2Solar is the mean solar time, the Equation of Time correction is not calculated with this function. The fSolI function includes this correction if desired.

If the index argument of CBIND is NULL (default) the first object of ... must be a zoo object.

Value

The function local2Solar produces a POSIXct object with its time zone set to UTC.

The function CBIND produces a zoo object.

The function lonHH gives a numeric value.

Note

It is important to note that the solaR package sets the system time zone to UTC with Sys.setenv(TZ = 'UTC'). Every zoo object created by the package will have an index with this time zone and will be supposed to be mean solar time.

Author(s)

Oscar Perpiñán Lamigueiro.

References

• Perpiñán, O, Energía Solar Fotovoltaica, 2015. (https://oscarperpinan.github.io/esf/)

• Perpiñán, O. (2012), "solaR: Solar Radiation and Photovoltaic Systems with R", Journal of Statistical Software, 50(9), 1-32, doi:10.18637/jss.v050.i09

Examples

t.local <- as.POSIXct("2006-01-08 10:07:52", tz = 'Europe/Madrid')

##The local time zone and the location have the same longitude (15 degrees)
local2Solar(t.local)
##But Madrid is at lon = -3
local2Solar(t.local, lon = -3)

##Daylight saving time
t.local.dst <- as.POSIXct("2006-07-08 10:07:52", tz = 'Europe/Madrid')

local2Solar(t.local.dst)
local2Solar(t.local.dst, lon = -3)

## Not run: 
##Extracted from an example of calcG0
##NREL-MIDC
##La Ola, Lanai
##Latitude: 20.76685o North
##Longitude: 156.92291o West
##Time Zone: -10.0

NRELurl <- 'http://goo.gl/fFEBN'

dat <- read.table(NRELurl, header = TRUE, sep = ',')
names(dat) <- c('date', 'hour', 'G0', 'B', 'D0', 'Ta')

##B is direct normal. We need direct horizontal.
dat$B0 <- dat$G0-dat$D0

##http://www.nrel.gov/midc/la_ola_lanai/instruments.html:
##The datalogger program runs using Greenwich Mean Time (GMT),
##data is converted to Hawaiin Standard Time (HST) after data collection
idxLocal <- with(dat, as.POSIXct(paste(date, hour), format = '%m/%d/%Y %H:%M', tz = 'HST'))
head(idxLocal)
idx <- local2Solar(idxLocal, lon = -156.9339)
head(idx)

## End(Not run)

Description

This function simulate the performance of a water pump fed by a frequency converter with several PV generators of different size during a day. The result is plotted as a nomogram which relates the nominal power of the PV generator, the total water flow and the total manometric head.

Usage

NmgPVPS(pump, Pg, H, Gd, Ta = 30,
    lambda = 0.0045, TONC = 47, eta = 0.95,
    Gmax = 1200, t0 = 6, Nm = 6,
    title = '', theme = custom.theme.2())

Arguments

Details

This function computes the irradiance profile according to the IEC 61725 "Analytical Expression for Daily Solar Profiles", which is a common reference in the official documents regarding PV pumping systems. At this version only pumps from the manufacturer Grundfos are included in pumpCoef.

Value

Author(s)

Oscar Perpiñán Lamigueiro.

References

• Abella, M. A., Lorenzo, E. y Chenlo, F.: PV water pumping systems based on standard frequency converters. Progress in Photovoltaics: Research and Applications, 11(3):179–191, 2003, ISSN 1099-159X.

• Perpiñán, O, Energía Solar Fotovoltaica, 2015. (https://oscarperpinan.github.io/esf/)

• Perpiñán, O. (2012), "solaR: Solar Radiation and Photovoltaic Systems with R", Journal of Statistical Software, 50(9), 1-32, doi:10.18637/jss.v050.i09

See Also

fPump, prodPVPS, pumpCoef

Examples

Pg = seq(4000, 8000,by = 100);
H = seq(120, 150,by = 5);

data(pumpCoef)

CoefSP8A44 <- subset(pumpCoef, Qn == 8 & stages == 44)

NmgSP8A44 <- NmgPVPS(pump = CoefSP8A44,Pg = Pg,H = H,Gd = 5000,
     title = 'Choice of Pump', theme = custom.theme())

Description

diff2Hours converts a difftime object into its numeric value with units = 'hours'.

char2diff converts a character description into a difftime object, following the code of seq.POSIXt.

sample2Hours calculates the sampling time in hours described by a character or a difftime.

P2E (power to energy) sums a series of power values (for example, irradiance) to obtain energy aggregation (for example, irradiation) using sample2Hours for the units conversion.

Usage

diff2Hours(by)
char2diff(by)
sample2Hours(by)
P2E(x, by)

Arguments

Value

A numeric value or a difftime object.

Author(s)

Oscar Perpiñán Lamigueiro

See Also

Sol

Examples

char2diff('min')
char2diff('2 s')

sample2Hours('s')
sample2Hours('30 m')

by1 <- char2diff('10 min')
sample2Hours(by1)

Description

Several small functions to convert angle units.

Usage

d2r(x)
r2d(x)
h2r(x)
h2d(x)
r2h(x)
d2h(x)
r2sec(x)