NEW SWARM INTELLIGENCE BASED OPTIMIZATION ALGORITHMS FOR THE OPTIMIZATION OF MICROGRIDS

Year 2018, Volume: 8 Issue: 2, 196 - 208, 29.12.2018

Tuba Tanyıldızı Ağır , Tuba Tanyıldızı Ağır

https://doi.org/10.36222/ejt.464197

Cited By: 2

Abstract

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

Keywords

Microgrid, Metaheuristic algorithms, SSO

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