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Solar–wind hybrid renewable energy system 233

Some researchers also explored other economical approaches, such as the Level-
ized Cost of System [11] and life-cycle cost [72].

4.2 Optimum sizing methods for hybrid solar–wind system
4.2.1 Probabilistic methods
Probabilistic methods are the simplest sizing methodologies. However, the results
attained by these techniques may not the most suitable to find out the best solution.
Usually, these techniques consider one or two system performance indicators to be
optimized to size the components of the studied system. Probabilistic approaches of
sizing hybrid solar–wind system consider the effect of the solar radiation and wind
speed variabilities for optimising the system design.
In their study, Bucciarelli [73] projected a sizing methodology treating storage
energy variation as a random walk. In some other studies, the probability density for
daily increment or decrement of storage level was estimated by a two-event probabil-
ity distribution [74]. To consider the effect of correlation between day to day radiation
values, this technique was further extended by Bucciarelli [75].
Gordon further modified the method suggested by Bucciarelli [76]. In their
study, the storage energy transitions were approximated by the three-event proba-
bilistic approach, which helped them to overcome the limitations of conventional
two-event approach in matching the actual distribution of the energy generated by
hybrid systems.
A probabilistic approach based on the convolution technique was presented by Tina
et al. [77]. Their study incorporates the fluctuating nature of the resources and the
load, eliminating the need for time-series data, to assess the long-term performance
of a hybrid solar wind system for both stand-alone and grid-connected applications.
In this study, the performance of the hybrid system is assessed by engaging proba-
bilistic models for both PV array and wind turbines. A numerical example applica-
tion has been illustrated in this study to demonstrate the validity of the developed
probabilistic model and the results are compared to those resulting from time-series
simulations. Disadvantage of this probabilistic approach is that it cannot characterize
the dynamic changing performance of the hybrid system.

4.2.2 Analytical methods
Analytical methods represent the hybrid energy systems by means of computational
models which describe hybrid system size as a function of its feasibility [66]. There-
fore, system’s performance can be measured for a set of possible system architecture
and/or a size of components. To determine the best configuration of a hybrid energy
system, single or multiple performance indexes of the system is/ are analysed. This
methodology permits the simulation of the performance of several hybrid system con-
figurations. However, this method requires long time series, usually 1 year, of weather
data (solar, and wind) for the simulations. The performance evaluation of hybrid sys-
tem can be analysed by computational models (i.e., commercial software tools and/