Multi-objective optimization of hybrid renewable energy system by using novel autonomic soft computing techniques

Abstract

An increasing demand in electric power consumption has clearly led to an exhaustion of alternating energy resources. Undoubtedly, it has harmful environmental effects. Hybrid energy and Micro grid can solve this kind of problem. The concept of micro grid is quite significant in cases where transmission of electric power is nither feasible nor profitable. An efficient scheduling of micro grid is able to meet load demand without shedding any load and the optimization is required to make it profitable and eco-friendly. In this regard this work implements a twenty four hours based environmental/economic scheduling of distributed generating units with renewable energy sources in a micro grid connected with main grid .This work proposes a framework for optimal scheduling of micro grid which minimize the cost of generating units as well as emission. Particle Swarm Optimization technique has been employed to solve this problem. Weighting factor is used for optimization in multi-objective framework where both cost and emission are minimized simultaneously. In this paper, a comparative study of employing different types of Particle Swarm Optimization has been made where Hierarchical Particle Swarm Optimization (HPSO) performs better incorporating different constraints. The results of proposed Particle Swarm Optimization method are compared and verified with results of others method which is recently employed. Finally, the comparative study indicates that proposed method gives superior solution than previous method in case of operating cost and emission.