Abstract
Different isolated systems with conventional generation sources are installed in Non-Interconnected Areas (ZNI) in Colombia while off-grid renewable systems are a trending answer for the energy supply in these regions. The complementarity between different energy sources, a storage system and adequate control can substantially improve the reliability of isolated generation systems. In this context, the sizing of a Hybrid Renewable Energy System (HRES) by means of a Genetic Algorithm (GA) is presented, considering the wind and solar resources specific to a representative rural location in Colombia. The methodology involves power curves for small wind turbines and the model for photovoltaic solar panels. The preliminary output consists of a weighted distribution for each technology, either wind or conventional photovoltaics, and is constrained by the Loss of Power Supply Probability (LPSP) and the Levelized Cost Of Electricity (LCOE). A second step consists of the optimization of the installed area for photovoltaic generation, considering a Concentrated Photovoltaic (CPV) system and aiming to maintain the initial fraction of generation for this resource. Finally, an analysis is performed on the reduction of area for solar generation to the increase in costs derived from the use of concentrators and other penalties associated with this technology.
Highlights
About 80% of the worldwide energy demand is supplied from fossil resources and only 20% from renewable sources, where hydroelectric power and biomass stand out
The results shown by Xuan et al [13] support the previous argument as they reveal an increase in maximum power of 158.7% under experimental tests
Most commercially available wind turbines are designed for relatively high rated wind speed, since no low-speed alternatives were available, the energy production cannot offset the costs of wind energy
Summary
About 80% of the worldwide energy demand is supplied from fossil resources and only 20% from renewable sources, where hydroelectric power and biomass stand out. Recent works in sizing of HRES use meta-heuristic methods as a way of finding a solution which is not necessarily the optimum but is an adequate approximation. Wind energy applications can often involve diverse phenomena, making meta-heuristic methods an attractive choice Two of these methods are Particle Swarm Optimization (PSO), inspired by the behavior of flocks of birds and fish [6] and GA which combines exhaustive search techniques and the natural principles of evolution [7]. Their popularity for sizing of HRES is highlighted in the work of Khare et al [8]. Kamjoo et al [11] uses a method for nondominant genetic classification that aims to minimize total system cost while maximizing reliability
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