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solaR (version 0.46)

A4_prodGCPV: Performance of a grid connected PV system.

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(),
         ...)

Value

A ProdGCPV object.

Arguments

lat

numeric, latitude (degrees) of the point of the Earth where calculations are needed. It is positive for locations above the Equator.

modeTrk

A character string, describing the tracking method of the generator. See calcGef for details.

modeRad, dataRad

Information about the source data of the global irradiation. See calcG0 for details.

sample, keep.night

See calcSol for details.

sunGeometry

character, method for the sun geometry calculations. See calcSol, fSolD and fSolI.

corr, f

See calcG0 for details.

betaLim, beta, alfa, iS, alb, horizBright, HCPV

See calcGef for details.

module

list of numeric values with information about the PV module,

Vocn

open-circuit voltage of the module at Standard Test Conditions (default value 57.6 volts.)

Iscn

short circuit current of the module at Standard Test Conditions (default value 4.7 amperes.)

Vmn

maximum power point voltage of the module at Standard Test Conditions (default value 46.08 amperes.)

Imn

Maximum power current of the module at Standard Test Conditions (default value 4.35 amperes.)

Ncs

number of cells in series inside the module (default value 96)

Ncp

number of cells in parallel inside the module (default value 1)

CoefVT

coefficient of decrement of voltage of each cell with the temperature (default value 0.0023 volts per celsius degree)

TONC

nominal operational cell temperature, celsius degree (default value 47).

generator

list of numeric values with information about the generator,

Nms

number of modules in series (default value 12)

Nmp

number of modules in parallel (default value 11)

inverter

list of numeric values with information about the DC/AC inverter,

Ki

vector of three values, coefficients of the efficiency curve of the inverter (default c(0.01, 0.025, 0.05)), or a matrix of nine values (3x3) if there is dependence with the voltage (see references).

Pinv

nominal inverter power (W) (default value 25000 watts.)

Vmin, Vmax

minimum and maximum voltages of the MPP range of the inverter (default values 420 and 750 volts)

Gumb

minimum irradiance for the inverter to start (W/m²) (default value 20 W/m²)

effSys

list of numeric values with information about the system losses,

ModQual

average tolerance of the set of modules (%), default value is 3

ModDisp

module parameter disperssion losses (%), default value is 2

OhmDC

Joule losses due to the DC wiring (%), default value is 1.5

OhmAC

Joule losses due to the AC wiring (%), default value is 1.5

MPP

average error of the MPP algorithm of the inverter (%), default value is 1

TrafoMT

losses due to the MT transformer (%), default value is 1

Disp

losses due to stops of the system (%), default value is 0.5

modeShd, struct, distances

See calcShd for details.

...

Additional arguments for calcG0 or calcGef

Author

Oscar Perpiñán Lamigueiro

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.

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, tools:::Rd_expr_doi("10.18637/jss.v050.i09")

See Also

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

Examples

Run this code
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')

if (FALSE) {
###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'))
}

###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'))

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