Page 257 - Hybrid-Renewable Energy Systems in Microgrids

234 Hybrid-Renewable Energy Systems in Microgrids

or numerical approximations of system components). Several computer tools are
available to assess hybrid energy performance, which aids the designer to analyse the
integration of renewable sources.
In a review conducted by Connolly et al., different computational simulation tools
of hybrid energy systems were analysed and their performance was compared [78].
The review reported a widely-used simulation tool for performance assessment of
hybrid energy systems called HOMER (Hybrid Optimization Model for Electric
Renewable), developed by the National Renewable Energy Laboratory (NREL), US.
Many studies have been reported in the literature for optimally sizing the components
of HRES using HOMER [79–82].

4.2.3 Iterative methods
In the iterative method, the performance assessment of HRES is done by means of
a recursive process which stops when the best configuration is reached according to
design specifications [66].
A Hybrid Solar– wind System Optimization (HSWSO) model was proposed by
Yang et al. [27], which utilizes the iterative optimization technique following the
LPSP model and LCE model for power reliability and system cost correspondingly.
The simulation considers three sizing, that is the capacity of PV system, rated power
of wind system, and capacity of the battery bank. For the desired LPSP value, the
optimum configuration can be determined by iteratively searching all the possible sets
of configurations to achieve the lowest LCE.
An iterative method that was investigated by Ashok, is reported in the literature
where an optimal hybrid system was designed among different renewable energy
combinations for a rural community. This study concentrates on minimizing the total
lifecycle cost, ensuring the reliability of the system [83]. This study uses a numerical
algorithm based on the Quasi-Newton method to solve the optimization problem.
Interactive Artificial intelligence (AI) methods, such as Genetic Algorithms (GA),
Artificial Neural Networks (ANN) and Fuzzy Logic, are widely used to optimize a
hybrid system to maximize its economic benefits [65]. Artificial intelligence means
the capability of a machine or object to perform similar kinds of functions that char-
acterise human thought [84].
GA is a stochastic global search and optimization technique which is inspired by
the process of natural evolution of species. GA is robust in finding globally optimal
solutions in the multi-modal and multi-optimization process [66]. GA is highly appli-
cable to cases of nonlinear systems, where finding
The global optimum is a difficult task. Due to the probabilistic development of
solutions GA is not constrained by local optimum, it can find the global optimum
system configuration with computational simplicity compared to other conventional
optimization methods such as dynamic programming and gradient techniques.
A methodology has been proposed by Koutroulis et al. [85] for optimum design
of a hybrid solar–wind system using GA. In this study, among a list of commercially
available system devices, the optimum number and type of units were selected, ensur-
ing that the 20 year-round total system cost is minimized subject to the constraint that
the load energy requirements are completely met.