Modeling, size optimization and sensitivity analysis of a remote hybrid renewable energy system

Sarangthem Sanajaoba Singh and Eugene Fernandez

Energy, 2018, vol. 143, issue C, 719-731

Abstract: Hybrid energy system based on solar and wind power coupled with energy storage unit provides a reliable and cost effective energy alternative above the commonly used diesel based standalone power system. Various methodologies are adopted for modeling hybrid energy system component. They are modeled either by deterministic or probabilistic methods. The current study considers the hardware failure of photovoltaic panels while modeling. A probability distribution (PD) represents the various capacity states due to hardware failure of photovoltaic panels and corresponding probabilities. A concept based on random number generation is adopted to calculate the actual hourly available photovoltaic power. Wind turbine power output modeling incorporate force outage rate of the turbine. A new meta-heuristic algorithm called Cuckoo Search is applied for solving the hybrid energy system optimization problem. Photovoltaic-Battery, Wind-Battery and Photovoltaic-Wind-Battery system applicable to a remote area located in Almora district of Uttarakhand, India are considered. The effectiveness of Cuckoo Search in solving hybrid system design problem is investigated and its performance is compared with other well known optimization algorithms like Genetic Algorithm and Particle Swarm Optimization algorithm. Furthermore, this paper investigates the sensitivity of various input parameters like solar, wind resources and capital cost on the cost of energy.

Keywords: Hybrid energy system; Cuckoo search; Genetic algorithm; Particle swarm optimization; Sensitivity (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (29)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:143:y:2018:i:c:p:719-731

DOI: 10.1016/j.energy.2017.11.053

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