LOAD SHEDDING USING GA AND ACO IN SMART GIRD ENVIRONMENT

Abstract

Increasing pressure on the utilities to accommodate energy efficiency, load management and progress in advanced technology has led to transformations for existing grid into a smarter grid. Creating awareness among the end-users to participate in load management programs instead of capacity addition is the best solution for maintaining the stability in the grid. Load shedding is a strategy under load management in which load connected to the smart grid is individually controlled via two- way communication. In this paper, a Smart Load shedding approach is developed based on load prioritization. The required amount of load to be shed under lack of sufficient generation level is optimized by Genetic Algorithm (GA) and Ant Colony Optimization (ACO) algorithms. The proposed approach is implemented using a real time feeder data from the substation, India. The results reflect the effectiveness of proposed algorithms taken into practical applications.