Fuzzy Logic for Solving the Water-Energy Management Problem in Standalone Water Desalination Systems: Water-Energy Nexus and Fuzzy System Design

Fuzzy Logic for Solving the Water-Energy Management Problem in Standalone Water Desalination Systems: Water-Energy Nexus and Fuzzy System Design

Ines Ben Ali (Ecole Nationale d'Ingénieurs de Tunis, LR11ES15 Laboratoire LSE, Université de Tunis El Manar, Tunisia), Mehdi Turki (École Supérieure des Ingénieurs de Medjez El Bab, Université de Jendouba, Tunisia), Jamel Belhadj (École Nationale Supérieure d'Ingénieurs de Tunis, Université de Tunis, Tunisia), and Xavier Roboam (LAPLACE, UMR CNRS-INP-UPS, Université de Toulouse, France)
Copyright: © 2023 |Pages: 28
DOI: 10.4018/IJFSA.317104
Article PDF Download
Open access articles are freely available for download


This work investigates an important topic of energy and water security (water-energy nexus). For this purpose, Water-Energy Management Strategy (W-EMS) for a standalone water desalination system powered by PV-Wind source is designed. The proposed W-EMS is based on fuzzy logic. In this context, authors focus on the design phase of the Fuzzy Inference System (FIS) through which three design methods are described and analyzed. The influence of FIS design on W-EMS performance is highlighted. First, it is shown that based on the designer's knowledge, the handmade-FIS can offer good performance for the W-EMS. Then, the water-energy management is formulated as an optimization problem. Therefore, genetic algorithm is used to optimize the FIS design to reduce iterative hand-tuning trials. Furthermore, the design of the fuzzy W-EMS can be addressed by a data-driven approach as a third step. This method shows its good performance in terms of water production and energy efficiency compared to the designed FISs by the two previous methods (handmade tuning and genetic algorithm).
Article Preview


Energy Management Strategies (EMSs) present a major concern for researchers (Cai et al., 2009) working on complex energy systems such as: decentralized generation systems (Ball et al., 2009), residential applications (Aki et al., 2006), block-chain for smart city (Orecchini et al., 2019), hybrid renewable energy system (Benmessaoud et al., 2019) as well as standalone water pumping/desalination systems (Sallem et al., 2009; Kyriakarakos et al., 2017). Therefore, several EMSs have been proposed in literature for various complex energy systems, while it is still difficult to determine the best approach in each situation. These EMSs can be classified into two categories: Rule-based and Optimization-based approaches (Salmasi, 2007). Rule-based energy management approaches are well known for their simplicity in implementing rules and their effectiveness for real-time supervisory control of energy flows into a complex energy system (Lee et al., 2000; Tekin et al., 2007). Such rules are designed based on intuition, heuristics, human expertise, even mathematical model, but without a prior knowledge of operating conditions (e.g. electric power profile, driving cycle for electric vehicles, etc.). The rule-based approaches, in turn, can be subdivided into: i) deterministic rule-based methods, and ii) fuzzy rule-based methods. Deterministic rules are generally implemented based on lookup tables (not real-time data) (Han et al., 2014; Garcia et al., 2009) to schedule energy flows between the system components. Fuzzy rule-based methods are widely adopted for complex real-time supervisory control issues enabling to realize a real-time and optimal power split (Arcos-Aviles et al., 2016; Tidjani et al., 2016; Tan et al., 2021; Mitiku et al., 2019; Chanda et al., 2019). Indeed, three main advantages of the fuzzy inference systems can be listed as follows: i) no real-time calculation issues, ii) robustness (tolerance to imprecise measurements), and iii) adaptation (easy tuning) with real-time parameters (Salmasi, 2007; Tie et al., 2013). This paper focuses on the fuzzy logic theory for energy management issue.

Fuzzy inference system is increasingly used and preferred for control and energy management issues for several complex energy systems, namely:

  • Micro-grids (Arcos-Aviles et al., 2016; Chen et al., 2012; Tidjani et al., 2016), and renewable energy generation systems (Cabrane et al., 2017; El Mokadem et al., 2009).

  • Transport domain, such as hybrid electric vehicles (Yin et al., 2016; Naffati et al., 2013; Xu et al. 2018), electric bus (Gao et al., 2008; Tian et al., 2017), electric aircraft (Zhang et al., 2010), electric traction (Talla et al., 2015), and electric ship (Khan et al., 2017).

  • Water pumping (Sallem et al., 2009; Yahyaoui et al., 2015) and desalination (Abdul-Fattah, 1981) systems.

Complete Article List

Search this Journal:
Volume 12: 1 Issue (2023)
Volume 11: 4 Issues (2022)
Volume 10: 4 Issues (2021)
Volume 9: 4 Issues (2020)
Volume 8: 4 Issues (2019)
Volume 7: 4 Issues (2018)
Volume 6: 4 Issues (2017)
Volume 5: 4 Issues (2016)
Volume 4: 4 Issues (2015)
Volume 3: 4 Issues (2013)
Volume 2: 4 Issues (2012)
Volume 1: 4 Issues (2011)
View Complete Journal Contents Listing