RT info:eu-repo/semantics/article
T1 Optimal generation scheduling in hydro-power plants with the Coral Reefs Optimization algorithm
A1 Gil Marcelino, Carolina
A1 Salcedo Sanz, Sancho
A1 Jiménez Fernández, Silvia
A1 Camacho Gómez, Carlos
K1 Generation scheduling
K1 Hydro-power plants
K1 Coral Reefs Optimization algorithm
K1 Meta-heuristics
K1 Bio-inspired algorithms
K1 Energy efficiency
K1 Informática
K1 Computer science
K1 Energías Renovables/Energías Alternativas
K1 Alternative energies
AB Hydro-power plants are able to produce electrical energy in a sustainable way. A known format for producing energy is through generation scheduling, which is a task usually established as a Unit Commitment problem. The challenge in this process is to define the amount of energy that each turbine-generator needs to deliver to the plant, to fulfill the requested electrical dispatch commitment, while coping with the operational restrictions. An optimal generation scheduling for turbine-generators in hydro-power plants can offer a larger amount of energy to be generated with respect to non-optimized schedules, with significantly less water consumption. This work presents an efficient mathematical modelling for generation scheduling in a real hydro-power plant in Brazil. An optimization method based on different versions of the Coral Reefs Optimization algorithm with Substrate Layers (CRO) is proposed as an effective method to tackle this problem.This approach uses different search operators in a single population to refine the search for an optimal scheduling for this problem. We have shown that the solution obtained with the CRO using Gaussian search in exploration is able to produce competitive solutions in terms of energy production. The results obtained show a huge savings of 13.98 billion (liters of water) monthly projected versus the non-optimized scheduling.
PB MDPI
SN 1996-1073
YR 2021
FD 2021-04-25
LK http://hdl.handle.net/10017/47548
UL http://hdl.handle.net/10017/47548
LA eng
NO European Commission
DS MINDS@UW
RD 29-abr-2024