TY - GEN
T1 - Low-Cost Embedded PSO for Decentralized, Scalable and Sustainable Hydroelectric Dispatch
AU - de Oliveira, Keila L.
AU - de Souza, Diego F. P.
AU - Webber, Thais
AU - Wanner, Elizabeth Fialho
AU - Marcelino, Carolina Gil
N1 - Copyright © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2026. This version of the article has been accepted for publication, after peer review and is subject to Springer Nature’s AM terms of use [ https://www.springernature.com/gp/open-research/policies/accepted-manuscript-terms ] but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https://doi.org/10.1007/978-3-032-25552-5_7
PY - 2026/7/2
Y1 - 2026/7/2
N2 - The increasing demand for electrical energy highlights the need for efficient operation of power generation systems. This study presents an embedded optimization device for hydroelectric dispatch developed on low-cost hardware. The proposed device introduces control strategies that conserve water resources while reliably meeting energy demands through a portable and efficient Particle Swarm Optimization (PSO) algorithm, adapted for micro- and resource-constrained hydroelectric applications. To validate its effectiveness, a Hydroelectric Power Plant (HPP) dispatch simulation model and the PSO algorithm were implemented and tested on embedded platforms (Arduino Mega 2560 and Raspberry Pi 3 Model B), with performance benchmarked against a high-performance computing machine. Results show that, despite computational limitations, embedded systems can effectively support hydroelectric dispatch planning, providing a cost-effective, scalable, decentralized, and sustainable alternative to traditional control solutions.
AB - The increasing demand for electrical energy highlights the need for efficient operation of power generation systems. This study presents an embedded optimization device for hydroelectric dispatch developed on low-cost hardware. The proposed device introduces control strategies that conserve water resources while reliably meeting energy demands through a portable and efficient Particle Swarm Optimization (PSO) algorithm, adapted for micro- and resource-constrained hydroelectric applications. To validate its effectiveness, a Hydroelectric Power Plant (HPP) dispatch simulation model and the PSO algorithm were implemented and tested on embedded platforms (Arduino Mega 2560 and Raspberry Pi 3 Model B), with performance benchmarked against a high-performance computing machine. Results show that, despite computational limitations, embedded systems can effectively support hydroelectric dispatch planning, providing a cost-effective, scalable, decentralized, and sustainable alternative to traditional control solutions.
KW - Edge Computing
KW - Embedded Optimization
KW - Low-Cost Hardware
KW - Micro Hydroelectric Dispatch
KW - Particle Swarm Optimization
UR - https://link.springer.com/chapter/10.1007/978-3-032-25552-5_7
UR - https://www.scopus.com/pages/publications/105047653617
U2 - 10.1007/978-3-032-25552-5_7
DO - 10.1007/978-3-032-25552-5_7
M3 - Conference publication
AN - SCOPUS:105047653617
SN - 9783032255518
T3 - Lecture Notes in Computer Science
SP - 101
EP - 118
BT - From Data to Models and Back: 13th International Symposium, DataMod 2025, Toledo, Spain, November 10–11, 2025, Revised Selected Papers
A2 - Lestingi, Livia
A2 - Salaün, Gwen
A2 - Requeno Jarabo, José Ignacio
PB - Springer, Cham
T2 - 13th International Symposium on From Data Models and Back, DataMod 2025
Y2 - 10 November 2025 through 11 November 2025
ER -