Maiden application of Cuckoo Search algorithm for optimal sizing of a remote hybrid renewable energy System

Sarangthem Sanajaoba and Eugene Fernandez

Renewable Energy, 2016, vol. 96, issue PA, 1-10

Abstract: Solar and wind power based hybrid energy system with energy storage unit provides a reliable and cost effective energy alternative above the conventional diesel generator based system commonly used by remote consumers. In this context, this paper explores the application of a new meta-heuristic algorithm called Cuckoo Search (CS) in the area of a hybrid energy system design problem. Cuckoo Search (CS) is applied for optimal sizing of three different system schemes viz. Photovoltaic-Battery, Wind-Battery and Photovoltaic-Wind-Battery system applicable to a remote area located in Almora district of Uttarakhand, India while minimizing total system cost and considering seasonal variation of load. The effectiveness of Cuckoo Search algorithm in solving hybrid energy system design problem is investigated and its performance is compared with other well known optimization algorithms like Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) algorithm. Optimization results further, shows that hybrid integration of photovoltaic, wind and battery storage gives most reliable and economical system scheme for the study area. In addition, this paper assesses the effect of wind turbine generator (WTG) force outage rate (FOR) on the optimal system reliability and economics.

Keywords: Hybrid energy system; Cuckoo search; Genetic algorithm; Particle swarm optimization; Wind turbine generator force outage rate (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (43)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0960148116303718
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:96:y:2016:i:pa:p:1-10

DOI: 10.1016/j.renene.2016.04.069

Access Statistics for this article

Renewable Energy is currently edited by Soteris A. Kalogirou and Paul Christodoulides

More articles in Renewable Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().