Page 24 - Hybrid-Renewable Energy Systems in Microgrids

Hybrid PV–wind renewable energy sources for microgrid application: an overview 9

A probabilistic approach in optimization may be the simplest and easiest method,
but this might not be able to find the best-optimal solution. Usually one or two system
performance probability functions are considered, mostly the solar radiation and wind
speed. Hybrid system optimization based on analytical method is solved by compu-
tational software such as HOMER and GAMES to find the feasibility if the system.
This allows multiple performance index analysis in multiple hybrid configurations.
However, long-term data can ensure higher accuracy of the system optimization.
There are several computer-based optimization software programs available to carry
out hybrid optimization. Nonetheless, not many studies have been done on comparing
the available software tool to find the most suitable one in terms of robustness, cal-
culation time, and complexity of the system configuration. Performance assessment
based on the iterative approach provides the facility to define several performance
indexes. Because online decision is made in iterative approach, it ensures the proper
balance within the design constraints. Nevertheless, considering many performance
indexes and design constraints can result in a very complex optimization process, but
this also can ensure higher accuracy of the optimization to achieve optimal size with
lowest cost and higher reliability of the system.
Multiple trade-off solutions are derived from a hybrid system based on multicri-
teria design constraints. Costs, reliability, and less-pollutant emissions are the objec-
tives of the hybrid system design adopted in Ref. [28] and the developed Modified
Particle Swarm Optimization algorithm (MPSO) provides a solution for incorporating
these objectives. Two different types of PV modules are considered to design optimal
configuration in Ref. [29] to minimize the total cost. The optimization formulation
is solved by Genetic Algorithm (GA), and it is shown that, to ensure there is no loss
in the load supply due to renewable variations, diesel generator is needed beside bat-
tery storage system (BSS). A Hybrid Genetic Algorithm (HGA) is used to study and
determine the optimal size to minimize the construction, operation, maintenance, and
replacement costs of four major locations in Japan [30]. A global optimization model
is developed in Ref. [31] and solved by the improved quantum evolution algorithm
(IQEA) to maximize the cost–benefit ratio in a long-run operation.
A comparative study of two algorithms, Invasive Weed Optimization (IWO) and
the hybrid IWO–PSO algorithm is carried out in Ref. [32] in terms of convergence
speed and optimality of the outcomes in reducing the cost of the system. A controller
design is a very important part of the optimal design process. A properly designed
controller can ensure efficient operation of the renewable sources, longer life of the
battery via optimal charging/discharging, and optimal support from the diesel genera-
tor to maintain reliability of the system at a low cost. A linear programming technique
is employed in Ref. [33] to minimize the regular electricity generation cost while
satisfying the reliable load demand requirement and monitor and control the operation
of the autonomous hybrid energy system.
An iterative algorithm is utilized to size a generation unit in Ref. [34] to reduce the
overall annual cost while meeting the total annual customer load demand. An economic
analysis and comparison has been discussed between renewable energy generations ver-
sus constructing a new transmission line to be connected with the nearest grid. Hybrid
Flower Pollination Algorithm (FPA) and Simulated Annealing (SA) algorithm are estab-