Page 253 - Hybrid-Renewable Energy Systems in Microgrids

230 Hybrid-Renewable Energy Systems in Microgrids

A new methodology for calculating the power of a wind turbine was presented
by Zamani and Riahy [56] considering wind speed variations. They assessed the
rate of wind speed variations by the energy pattern factor (EPF) of actual wind
and evaluated the performance of rotor speed and pitch angle controllers by using
a new factor, which is called wind turbine controllability (Ca). Using EPF and
Ca, the power curve is modified considering the extra power captured by the
controllers.

3.3 Modelling of battery storage system
The integration of renewable energies possesses some technical and economic prob-
lems. These kinds of energy cannot be easily stored or transported like conventional
fossil and nuclear fuels. In addition to that, their supply can be extremely irregular and
variable. Hence, storage systems or batteries are required to compensate the irregulari-
ties in the solar and wind power distributions.
For many years, researchers have concentrated on the development of the battery
behavioural model. Kim and Hong [43] analysed the discharge performance of a
flooded lead–acid battery cell using mathematical modelling. Their work was inspired
by the studies conducted by Gu et al. [57] and Ekdunge and Simonsson [58]. Ekdunge
and Simonsson analysed the incorporation of the diffusion–precipitation mechanism
in the kinetics reaction of the negative electrode.
A mathematical model of lead–acid batteries has been developed by Bernardi and
Carpenter [59] by adding the oxygen recombination reaction. Nguyen et al. [60] sug-
gested a model equivalent to the flooded type and investigated the dynamic behaviour
of the cell during discharge with respect to cold cranking amperage and reserve capac-
ity. These suggested battery bank models are complex in terms of the expressions and
number of parameters involved. Determination of many of these parameters is done
by measuring the internal components or by the extensive investigation. Therefore,
these models are subject to be used to evaluate the theoretical characteristics of battery
designs and are not practical for simulating the performance of a random battery at
random operating conditions.
Some other studies reported in literature presents battery behavioural prediction
approaches that include charge accumulation and experimental models. In their study,
Yang et al. [27] have shown that a lead–acid battery can be characterized by two
indexes: state of charge (SOC) and the floating charge voltage or the terminal volt-
age.Morgan et al. [61] analysed the performance of battery units in an autonomous
hybrid energy system at different temperatures considering the state of voltage (SOV)
instead of SOC. Models have been presented in the literature on floating charge volt-
age simulations, [62] describing the relationship between the floating charge voltage,
the current rate and the battery SOC.
Various studies have shown that the battery charge is a complex function of the
battery’s operating conditions. Hence, it is important to determine the correction fac-
tors experimentally [63].