Optimization of Hybrid Renewable Energy Sources Using Metaheuristic Algorithms

Abstract

The need for power is rising on a daily basis all across the world. Due to the finite supply of fossil fuels, it is critical to develop innovative non-renewable energy systems that can reduce reliance on conventional energy sources. Solar photovoltaic (PV) is most common alternative renewable energy source due to availability in abundance. Recently, Proton Exchange Membrane Fuel Cells (PEMFCs) is also gaining popularity as the feasible method for sustainable power generation to meet the ever-increasing electricity demand with no greenhouse gases emission. The optimal operation of PV and PEMFC needs to be ensured to exploit its advantages to the maximum limit. In this direction, there is an extensive need for parameter extraction of PV and PEMFC. Many researchers have applied evolutionary optimization approaches are used to estimate the PV and PEMFCs parameters as the precise modeling of these cells still to deliver good model. In all engineering areas, optimization is a frequent mathematical issue. Finding the finest possible/desirable option is what it actually implies. Optimization issues are so diverse and numerous, strategies for tackling them should be an emerging research area. The nature of optimization methods can be stochastic. As computationally efficient alternatives to deterministic approaches, bio-inspired stochastic optimization algorithms have been developed. Meta-heuristics are based on the iterative improvement of a population of solutions (as in Evolutionary algorithms and Swarm based algorithms) or a single solution and often use randomization and local search to solve an optimization issue. In this thesis, the work objective is to enhance the optimization algorithm to overcome the process's variety and premature convergence. This can be possible by modification meta-heuristic algorithm by using chaos theory or hybridization of algorithm. In a nonlinear system, chaos is a deterministic and random process that refers to initial conditions. The behaviour of the nonlinear system will dramatically change if there are small changes in the initial values. Besides, a chaotic system has complex characteristics such as certainty, randomness, and sensitivity initial conditions, and even a good internal structure. Based on these features, the diversity of the population should be retained and therefore prevent entering into an optimum local search and improve the probability of reaching a global optimum. This feature of chaos theory is explored on different optimization algorithm to increase the accuracy of parameter extraction of PV and PEMFC.

A hybrid off-grid renewable energy system is needed to reduce reliance on traditional energy supplies, and to enhance the reliability of the renewable energy system. The process of selecting the appropriate combinations of components and their costs in order to produce an affordable, dependable and effective alternative energy supply is known as hybrid system optimization. Hybrid energy technology can meet the energy needs of community very effectively. The goal of improving hybrid energy system control, size, and component selection is to offer society with a cost-effective electric power solution. One of the objective of this thesis aims to use the proposed hybrid chaotic algorithm for optimal sizing of hybrid renewable energy system. The solar/fuel cell/biomass hybrid renewable energy system is considered for the continuation of supply. The outcome of the proposed algorithm and parent algorithm is compared with Hybrid Optimization of Multiple Energy Resources (HOMER) software.