An efficient multi-objective evolutionary approach for solving the operation of multi-reservoir system scheduling in hydro-power plants

C.G. Marcelino, G.M.C. Leite, C.A.D.M. Delgado, L.B. de Oliveira, E.F. Wanner, S. Jiménez-Fernández, S. Salcedo-Sanz

    Research output: Contribution to journalArticlepeer-review


    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.
    Original languageEnglish
    Article number115638
    Number of pages18
    JournalExpert Systems with Applications
    Early online date28 Jul 2021
    Publication statusPublished - 15 Dec 2021

    Bibliographical note

    Copyright © 2021 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (

    Funding & Acknowledgements: This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 754382. This research has been partially supported by Ministerio de Economía y Competitividad of Spain (Grant Ref. TIN2017-85887-C2-2-P) and by Comunidad de Madrid, PROMINT-CM project (grant No. P2018/EMT-4366). The authors thank UAH, UFRJ and CEFET-MG for the infrastructure used to conduct this work, and Brazilian research agencies: CAPES (Finance Code 001) and CNPq for support.


    • Cascading hydro-power plant modeling
    • Multi-objective optimization
    • Swarm intelligence
    • MESH
    • Energy production


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