Improved Smell Agent Optimization Sizing Technique Algorithm for a Grid-Independent Hybrid Renewable Energy System

Akawu Shekari Biliyok and Salawudeen Ahmed Tijani

A chapter in Renewable Energy - Recent Advances from IntechOpen

Abstract: This chapter discuss an improvement on the novel computational intelligent algorithm using the smell phenomenon. In the standard smell agent optimization algorithm, the olfactory capacity is constant thereby assuming that every smell agent has the same sensing capacity. In the improved smell agent optimization algorithm, that is changed to account for the difference in smell agent capacity. The algorithm was run against the standard smell agent optimization on Matlab to find the best HRES design using annual cost, Levelized cost of electricity (LCE), loss of power supply probability (LPSP) and excess energy. It was shown after the comparative analysis that there was a 79%, 99.9% and 53.4% improvement for annual cost, LCE and LPSP respectively. Statistically, results showed that the iSAO obtained the most cost effective HRES design compared to the benchmarked algorithms.

Keywords: smell agent optimization (SAO); improved smell agent optimization (iSAO); hybrid renewable energy system (HRES); levelized cost of electricity (LCE); loss of power supply probability (LPSP) (search for similar items in EconPapers)
JEL-codes: Q20 Q40 (search for similar items in EconPapers)
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Persistent link: https://EconPapers.repec.org/RePEc:ito:pchaps:269622

DOI: 10.5772/intechopen.105489

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