Cost Minimization in a Smart Grid Using Particle Swarm Optimization Algorithm

Abstract

Global energy consumption is rising as a result of an aging population and more advanced lifestyles. Making sufficient use of all available energy sources, especially renewable ones, is essential to a nation's long-term economic viability and progress. In less developed nations, cost reduction and optimization-based energy management are crucial to resolving energy challenges. This study develops a dynamic energy management approach for smart grids that is optimization-based and based on the combination of renewable resource availability and schedulable customer demand, which is unique to developing nations. Based on demographic characteristics, consumer demand is categorized into two types: highly changeable loads and non-schedulable loads. In this study, we created a dynamic particle swarm optimization method that, by taking into account the available resources (Grid and Renewable), best plans the supply of energy to different customers. By ensuring a steady supply of energy while protecting customer comfort and grid stability, the suggested approach offers an affordable option. The results of the simulation utilizing PSO with different intermittent resource availability demonstrate cost savings for a user with variable energy consumption. The suggested approach is all-encompassing and may be used in different developing areas with comparable consumer demand and data.