prodGCPV: Performance of a grid connected PV system.
In solaR: Radiation and Photovoltaic Systems

Description Usage Arguments Details Value Author(s) References See Also Examples

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

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(),
         ...)

`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`

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.

A ProdGCPV object.

Oscar Perpiñán Lamigueiro

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

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

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'))