2022
Dijk, Marco; Vuuren, Stefanus Johannes; Cavazzini, Giovanna; Niebuhr, Chantel Monica; Santolin, Alberto
Optimizing Conduit Hydropower Potential by Determining Pareto-Optimal Trade-Off Curve Journal Article
In: Sustainability (Switzerland), vol. 14, iss. 13, 2022, ISSN: 20711050.
Abstract | Links | BibTeX | Tags: conduit hydropower, energy recovery, Genetic Algorithm, optimized energy generation, pareto optimality, renewable energy
@article{vanDijk2022,
title = {Optimizing Conduit Hydropower Potential by Determining Pareto-Optimal Trade-Off Curve},
author = {Marco Dijk and Stefanus Johannes Vuuren and Giovanna Cavazzini and Chantel Monica Niebuhr and Alberto Santolin},
doi = {10.3390/su14137876},
issn = {20711050},
year = {2022},
date = {2022-01-01},
journal = {Sustainability (Switzerland)},
volume = {14},
issue = {13},
abstract = {In numerous locations of bulk water supply/distribution systems, energy is dissipated by pressure-reducing devices, whereas it could be recovered by means of turbines or pumps as turbines. These pipe systems, owned and operated by municipalities, water utilities, large water-consuming industries, and mines, could be used as a source of renewable sustainable energy. However, the exploitation of these systems presents several issues related to the complexity of the operational optimization of the hydropower generation facilities and to the potential negative impact on the reliability of the system itself. We have developed a novel procedure to optimize the energy generation in such a conduit system by assessing the interrelationship of storage volumes, demand patterns, operating cycles, and electricity tariff structures. The procedure is a multi-objective genetic algorithm designed to provide a solution to maximize electricity generation and thus revenue and to minimize the risk involved in supplying the demand. A Pareto-optimal trade-off curve is set up, indicating the potential benefit (revenue) versus the reliability index (supply security). The results indicate that a Pareto-optimal trade-off curve was generated from which a solution could be selected which would improve the weekly revenue by up to 7.5%, while still providing a reliable water supply system.},
keywords = {conduit hydropower, energy recovery, Genetic Algorithm, optimized energy generation, pareto optimality, renewable energy},
pubstate = {published},
tppubtype = {article}
}
In numerous locations of bulk water supply/distribution systems, energy is dissipated by pressure-reducing devices, whereas it could be recovered by means of turbines or pumps as turbines. These pipe systems, owned and operated by municipalities, water utilities, large water-consuming industries, and mines, could be used as a source of renewable sustainable energy. However, the exploitation of these systems presents several issues related to the complexity of the operational optimization of the hydropower generation facilities and to the potential negative impact on the reliability of the system itself. We have developed a novel procedure to optimize the energy generation in such a conduit system by assessing the interrelationship of storage volumes, demand patterns, operating cycles, and electricity tariff structures. The procedure is a multi-objective genetic algorithm designed to provide a solution to maximize electricity generation and thus revenue and to minimize the risk involved in supplying the demand. A Pareto-optimal trade-off curve is set up, indicating the potential benefit (revenue) versus the reliability index (supply security). The results indicate that a Pareto-optimal trade-off curve was generated from which a solution could be selected which would improve the weekly revenue by up to 7.5%, while still providing a reliable water supply system.
