A study on optimum utilization of hybrid renewable energy systems using nature inspired algorithms

Abstract

newline Energy plays a vital role for the sustainable development of a Nation. Due to the increase in human population, increase in oil price, depletion of fossil fuels and increase in energy demand, renewable energy forms the alternative for conventional energy sources. Similar to conventional energy sources, Renewable Energy Sources (RES) offer several advantages like reduced pollution, cost effectiveness, environment friendliness, higher degree of sustainability, etc. Smart Grid (SG) is another rising aspect of power industry. It provides solutions to problems that exists with the conventional energy systems and targets sustainable, reliable and safe power to all the consumers. This research addresses one of the above issues i.e., socioeconomic issue and provides solution by proposing algorithms for better economical management. The first chapter of the thesis presents various SG connected systems, whereas the second chapter portrays the various methods of optimizing the SG. Even though integration of HRES is the part of the SG in the recent days, the proposed research focuses on delivering the power to the local load under consideration. The third chapter describes the mathematical modelling of the HRES and the fourth chapter explains a Particle Swarm Optimization (PSO) - Genetic Algorithm (GA) technique based economical management of HRES. The primary goal is to reduce the cost of energy and computational time using multi-objective PSO-GA technique for the optimum utilization of hybrid renewable energy systems. When the results of the PSO-GA technique are compared against the conventional methods, the PSO-GA provides optimal solution for satisfying the load demand. But, this method has its own limitations such as convergence speed and also provides better scope for further cost minimization. In the fifth chapter, the Whale Optimization Algorithm (WOA) is suggested for optimal usage of hybrid renewable energy sources.