Optimized fuzzy rule-based energy management for a battery-less PV/wind-BWRO desalination system

I. Ben Ali, M. Turki, J. Belhadj and X. Roboam

Energy, 2018, vol. 159, issue C, 216-228

Abstract: Coupling water desalination processes with Renewable Energy Sources (RESs) can be a sustainable and ecological approach to the global water/energy supply crisis. In this regard, small-scale standalone battery-less Brackish Water Reverse Osmosis (BWRO) desalination system powered by a hybrid PV/Wind RES is expected to meet freshwater demand of a small isolated community. One particularity of the proposed architecture deals with the absence of electrochemical storage; only taking benefit of hydraulic storage in water tanks when RE is available. This study puts forward the prime importance of Water/Power flows management optimization. For this purpose, an online Fuzzy Logic-based Energy Management Strategy (FLEMS) is proposed. Firstly, a Hand-Made Fuzzy Inference System (HMFIS) was designed to identify the instantaneous power sharing between the system hydro-mechanical processes (well pumping, water storage and desalination processes). Secondly, an offline genetic algorithm optimization was applied on the HMFIS design in order to optimize the power sharing factor and maximize freshwater production. Then, when applying unknown power profile, the optimized FLEMS demonstrated its performance to improve the system energy efficiency and enhance the brackish water (from 16.7% in autumn to 63% in summer) and freshwater production (to 3.3% during autumn for instance) compared with the HMFIS-based EMS.

Keywords: Water/energy production; Renewable energy sources; Energy management; Fuzzy logic; Genetic algorithm optimization (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (9)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544218311757
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:energy:v:159:y:2018:i:c:p:216-228

DOI: 10.1016/j.energy.2018.06.110

Access Statistics for this article

Energy is currently edited by Henrik Lund and Mark J. Kaiser

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