TY - GEN
T1 - A Hybrid Multiobjective Solution for the Short-term Hydro-power Dispatch Problem
T2 - 2021 IEEE Congress on Evolutionary Computation, CEC 2021
AU - Marcelino, Carolina G.
AU - de Oliveira, Lucas B.
AU - Wanner, Elizabeth F.
AU - Delgado, Carla A.D.M.
AU - Jiménez-Fernández, Silvia
AU - Salcedo-Sanz, Sancho
PY - 2021/8/9
Y1 - 2021/8/9
N2 - The unit dispatch problem is defined as the attribution of operational values to each generation unit inside a hydropower plant (HPP), given some criteria such as the total power to be generated, or the operational bounds of each unit. An optimal dispatch programming for hydroelectric units in HPP provides a larger production of electricity, with minimal water use. This paper presents an evolutionary approach to optimize the multicriteria electric dispatch problem in a general HPP, based on a Multi-objective Evolutionary Swarm Hybridization (MESH) algorithm. The proposed approach integrates mathematical models and evolutionary swarm computation. The experimental analysis shows that the proposed MESH algorithm is able to reach competitive results when compared with classical evolutionary algorithms, the NGA-II and SPEA2 basing on ANOVA inference test. Results also show that the proposed MESH is able to save a large amount of water in the energy production process, supplying the requested load, and minimizing blackout risks and generating a profit around $275,000 monthly.
AB - The unit dispatch problem is defined as the attribution of operational values to each generation unit inside a hydropower plant (HPP), given some criteria such as the total power to be generated, or the operational bounds of each unit. An optimal dispatch programming for hydroelectric units in HPP provides a larger production of electricity, with minimal water use. This paper presents an evolutionary approach to optimize the multicriteria electric dispatch problem in a general HPP, based on a Multi-objective Evolutionary Swarm Hybridization (MESH) algorithm. The proposed approach integrates mathematical models and evolutionary swarm computation. The experimental analysis shows that the proposed MESH algorithm is able to reach competitive results when compared with classical evolutionary algorithms, the NGA-II and SPEA2 basing on ANOVA inference test. Results also show that the proposed MESH is able to save a large amount of water in the energy production process, supplying the requested load, and minimizing blackout risks and generating a profit around $275,000 monthly.
UR - https://ieeexplore.ieee.org/document/9504898
UR - http://www.scopus.com/inward/record.url?scp=85118115015&partnerID=8YFLogxK
U2 - 10.1109/CEC45853.2021.9504898
DO - 10.1109/CEC45853.2021.9504898
M3 - Conference publication
AN - SCOPUS:85118115015
T3 - 2021 IEEE Congress on Evolutionary Computation, CEC 2021 - Proceedings
SP - 193
EP - 200
BT - 2021 IEEE Congress on Evolutionary Computation, CEC 2021 - Proceedings
PB - IEEE
Y2 - 28 June 2021 through 1 July 2021
ER -