Solar energy collection and storage - Solar Energy Sciences and Engineering Applications - page 143

2008), domestic space and water heating (Letz et al., 2009; Yazdanshanas and Furbo,
2008; Yazdanshanas et al., 2008). They can be augmented by the inclusion of heat
pumps (Troi et al., 2008; Huang and Lee, 2005; Morrison, 1984).
A wide variety of methodologies are available for the sizing of system components
and determining the optimal operating parameters to satisfy a known set of characteris-
tics of the energy load (Norton et al., 2001). These methodologies include: utilizability
(Collares-Pereira et al., 1984) empirical correlations (Gopffarth et al., 1968), simplified
analysis, semi-analytical simulation, stochastic simulation, simplified representative-
day simulations (Garg et al., 1984) and detailed hour-by-hour simulations (Morrison
and Braun, 1985; Hobson and Norton, 1988a). Each of these will be considered
individually.
There is a minimum threshold insolation at which the solar heat gained by a
collector corresponds to its heat losses at a particular ambient temperature. Only
above this minimum insolation threshold does the collector supply a useful heat yield.
Utilizability is a statistical attribute of the location-specific variation of insolation over
a given duration. For example hourly utilizability is the fraction of hourly incident
insolation that can be converted to heat by a collector with ideal heat removal and no
optical losses. As all solar collectors have heat losses, utilizability always has a value of
less than one. Utilizability can be related to other statistical properties of diurnal and
annual patterns of insolation (Reddy, 1987) to produce mathematical terms to which
specific collector parameters can be attached. Various generalised expressions have
thus been be derived, for example, for the yearly total energy delivered by flat plate
collectors whose tilt angles equalled the latitude of their notional location (Rabl, 1985).
This methodology can be very useful in initial design, but limitations include possible
inaccuracy of underlying insolation data correlations particularly when extended to
new locations collector inclinations and orientations. The technique has been applied
to interseasonal storage (Braun et al., 1981), where due to the very large thermal store
mass required, collector inlet temperatures are invariant.
Correlation-based system design techniques are predicated on the high proba-
bility that for a given solar energy process heat system, in a given period. More
insolation will lead to solar energy satisfying a larger share of the heat load. A dimen-
sionless or normalised solar energy input has usually been plotted against a similarly
parameterised output for a given system configuration from which correlations were
obtained.
Simplified analyses consider solely the key driving parameters of system perfor-
mance assuming all other variables remain constant (Braun et al., 1981). For solar
industrial heat loads that over the operating period have largely constant flow rates and
temperatures, simplified analysis have been developed that can employed for feasibil-
ity and initial design of industrial hot water system with heat storage (Collares-Pereira
et al., 1984). Simplified analyses maintain a physical basis for the relationships between
parameters that is lost in empirical correlations whose equations are polynominal
curve fits.
Semi-analytical simulation use detailed numerical models. However rather than
undertaking hour-by-hour calculations using insolation, ambient temperature and load
data, in this approach sinusoidal and linear functions are used to describe the insolation
and load respectively with ambient temperature either varying sinusoidally or remain-
ing constant. This approach has largely been superseded by hour-by-hour analysis as