NEW SWARM INTELLIGENCE BASED OPTIMIZATION ALGORITHMS FOR THE OPTIMIZATION OF MICROGRIDS

Abstract

The need for new energy sources has increased due to reasons such as thedevelopment of technology, the increase in electricity demand, the decrease offossil resources, and environmental pollution. Renewable energy sources areself-renewing, friendly, and clean energy sources. Microgrids are small powerenergy networks consisting of renewable and non-renewable energy sources,batteries, inverters, and loads. They can be operated connected to the networkand independently from the network. Metaheuristic methods are algorithms thatcan achieve optimum results in the search space. In this study, optimization of amicrogrid composed of a wind turbine, solar panel, diesel generator, inverter,and loads has been investigated with multi-objective hybrid metaheuristicalgorithms. Optimization is aimed at reducing emissions, increasing reliability,and optimizing energy resources. Swallow Swarm Optimization (SSO) andHybrid Particle Swallow Swarm Optimization (HPSSO) with different iterationsand populations are compared for the first time.