RT info:eu-repo/semantics/article
T1 An efficient multi-objective evolutionary approach for solving the operation of multi-reservoir system scheduling in hydro-power plants
A1 Gil Marcelino, Carolina
A1 Matos Cardoso Leite, Gabriel
A1 Delgado, C.A.D.M.
A1 Oliveira, L.B. de
A1 Fialho Wanner, Elizabeth
A1 Jiménez Fernández, Silvia
A1 Salcedo Sanz, Sancho
K1 Cascading hydro-power plant modeling
K1 Multi-objective optimization
K1 Swarm intelligence
K1 MESH
K1 Energy production
K1 Informática
K1 Computer science
K1 Energías Renovables/Energías Alternativas
K1 Alternative energies
AB This paper tackles the short-term hydro-power unit commitment problem in a multi-reservoir system ? a cascade-based operation scenario. For this, we propose a new mathematical modeling in which the goal is to maximize the total energy production of the hydro-power plant in a sub-daily operation, and, simultaneously, to maximize the total water content (volume) of reservoirs. For solving the problem, we discuss the Multi-objective Evolutionary Swarm Hybridization (MESH) algorithm, a recently proposed multi-objective swarm intelligence-based optimization method which has obtained very competitive results when compared to existing evolutionary algorithms in specific applications. The MESH approach has been applied to find the optimal water discharge and the power produced at the maximum reservoir volume for all possible combinations of turbines in a hydro-power plant. The performance of MESH has been compared with that of well-known evolutionary approaches such as NSGA-II, NSGA-III, SPEA2, and MOEA/D in a realistic problem considering data from a hydro-power energy system with two cascaded hydro-power plants in Brazil. Results indicate that MESH showed a superior performance than alternative multi-objective approaches in terms of efficiency and accuracy, providing a profit of $412,500 per month in a projection analysis carried out.
PB Elsevier
SN 0957-4174
YR 2021
FD 2021-12-15
LK http://hdl.handle.net/10017/49807
UL http://hdl.handle.net/10017/49807
LA eng
NO European Commission
DS MINDS@UW
RD 19-abr-2024