TY - JOUR
T1 - A multi-objective approach for location and layout optimization of wave energy converters
AU - Shadmani, Alireza
AU - Reza Nikoo, Mohammad
AU - Etri, Talal
AU - Gandomi, Amir H.
N1 - Publisher Copyright:
© 2023 Elsevier Ltd
PY - 2023/10/1
Y1 - 2023/10/1
N2 - Wave Energy Converters (WECs) have been increasingly installed in various coastal regions due to the higher energy density of waves than other renewable sources. Regarding coastal regions’ high potential, it is remarkably better to emplace multiple devices concerning a layout of the arrays with different configurations. Although WECs can capture the highest energy in hotspots, the location for installing these devices must be optimized. Purposefully, the wave propagation model, SWAN, was first employed respected to stochastic wind data to assess the wave energy potential and compute the annual energy production (AEP). The Latin Hypercube Sampling (LHS) technique was used to generate samples. Finally, the optimal location and layout of the arrays were determined through the multi-objective optimization (MOOP) algorithm, NSGA-III, based on SWAN's sequential outputs. The optimized layouts contained arrays with 4-, 8-, and 16-devices with regard to devices’ initial state. Almost 20 hotspots were located by solving the Pareto-front. It was found that the best arrangement for the 4-device arrays is linear. However, the optimal arrangement of the 8- and 16-device arrays widely varies and depends on the AEP of the region. Nevertheless, it seems best to position the 16-device array in a diagonal layout with one to three rows.
AB - Wave Energy Converters (WECs) have been increasingly installed in various coastal regions due to the higher energy density of waves than other renewable sources. Regarding coastal regions’ high potential, it is remarkably better to emplace multiple devices concerning a layout of the arrays with different configurations. Although WECs can capture the highest energy in hotspots, the location for installing these devices must be optimized. Purposefully, the wave propagation model, SWAN, was first employed respected to stochastic wind data to assess the wave energy potential and compute the annual energy production (AEP). The Latin Hypercube Sampling (LHS) technique was used to generate samples. Finally, the optimal location and layout of the arrays were determined through the multi-objective optimization (MOOP) algorithm, NSGA-III, based on SWAN's sequential outputs. The optimized layouts contained arrays with 4-, 8-, and 16-devices with regard to devices’ initial state. Almost 20 hotspots were located by solving the Pareto-front. It was found that the best arrangement for the 4-device arrays is linear. However, the optimal arrangement of the 8- and 16-device arrays widely varies and depends on the AEP of the region. Nevertheless, it seems best to position the 16-device array in a diagonal layout with one to three rows.
KW - Latin hypercube sampling
KW - MOOP
KW - NSGA-III
KW - SWAN
KW - Wave energy
KW - Wave generation
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UR - https://www.mendeley.com/catalogue/d5d48338-ab45-3c17-bfd0-d1d35c0a2611/
U2 - 10.1016/j.apenergy.2023.121397
DO - 10.1016/j.apenergy.2023.121397
M3 - Article
AN - SCOPUS:85162179318
SN - 0306-2619
VL - 347
JO - Applied Energy
JF - Applied Energy
M1 - 121397
ER -