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 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

References

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