Multi-objective genetic algorithm based sizing optimization of a stand-alone wind/PV power supply system with enhanced battery/supercapacitor hybrid energy storage

Abbassi Abdelkader, Abbassi Rabeh, Dami Mohamed Ali and Jemli Mohamed

Energy, 2018, vol. 163, issue C, 351-363

Abstract: The present paper proposes a new approach to optimize the sizing of a multi-source PV/Wind with Hybrid Energy Storage System (HESS). Hence, a developed modeling of all sub-systems composing the integral system has been designed to establish the proposed optimization algorithm. Besides, a frequency management based on Discrete Fourier Transform (DFT) algorithm has been also used to distribute the power provided by the power supply system into different dynamics. Thus, many frequency channels have been obtained in order to divide the roles of each storage device and show the impact of integrating fast dynamics into renewable energies based applications. The reformulation of our optimization problem is considered by the minimization of the Total Cost of Electricity (TCE) and the Loss of Power Supply Probability (LPSP) of the load, simultaneously. In this respect, a multi-objective based Genetic Algorithm approach was used to size the developed system considering all storage dynamics. In order to achieve an optimal system configuration, different economic analysis cases were established. The obtained results show that the minimum of LPSP is achieved according to a very low TCE which introduces that the exploitation of renewable energy has a very important effect to promote the energy sector in Tunisia.

Keywords: Renewable energy sources; Multi-objective genetic algorithm; Optimization; Sizing; Hybrid energy storage system; Economic analysis (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (35)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:163:y:2018:i:c:p:351-363

DOI: 10.1016/j.energy.2018.08.135

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