Smart Hydropower Water Distribution Networks, Use of Artificial Intelligence Methods and Metaheuristic Algorithms to Generate Energy from Existing Water Supply Networks

Diamantis Karakatsanis and Nicolaos Theodossiou
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Diamantis Karakatsanis: Division of Hydraulics and Environmental Engineering, Department of Civil Engineering, Aristotle University of Thessaloniki, 541 24 Thessaloniki, Greece
Nicolaos Theodossiou: Division of Hydraulics and Environmental Engineering, Department of Civil Engineering, Aristotle University of Thessaloniki, 541 24 Thessaloniki, Greece

Energies, 2022, vol. 15, issue 14, 1-21

Abstract: In this paper, the possibility of installing small hydraulic turbines in existing water-supply networks, which exploit the daily pressure fluctuations in order to produce energy, is examined. For this purpose, a network of five pressure sensors is developed, which is connected to an artificial intelligence system in order to predict the daily pressure values of all nodes of the network. The sensors are placed at the critical nodes of the network. The locations of the critical nodes are implemented by applying graph theory algorithms to the water distribution network. EPANET software is used to generate the artificial intelligence training data with an appropriate external call from a Python script. Then, an improvement model is implemented using the Harmony Search Algorithm in order to calculate the daily pressure program, which can be allocated to the turbines and, consequently, the maximum energy production. The proposed methodology is applied to a benchmark water supply network and the results are presented.

Keywords: WDNs; harmony search algorithm; machine learning; graph theory; micro hydro generator; wireless sensor network (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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