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Optimal Generation Scheduling in Hydro-Power Plants with the Coral Reefs Optimization Algorithm

Author

Listed:
  • Carolina Gil Marcelino

    (Department of Signal Processing and Communications, Universidad de Alcalá, Alcalá de Henares, 28805 Madrid, Spain
    Institute of Computing, Federal University of Rio de Janeiro, Rio de Janeiro 21941-972, Brazil)

  • Carlos Camacho-Gómez

    (Department of Information Systems, Universidad Politécnica de Madrid, Campus Sur, 28031 Madrid, Spain)

  • Silvia Jiménez-Fernández

    (Department of Signal Processing and Communications, Universidad de Alcalá, Alcalá de Henares, 28805 Madrid, Spain)

  • Sancho Salcedo-Sanz

    (Department of Signal Processing and Communications, Universidad de Alcalá, Alcalá de Henares, 28805 Madrid, Spain)

Abstract

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.

Suggested Citation

  • Carolina Gil Marcelino & Carlos Camacho-Gómez & Silvia Jiménez-Fernández & Sancho Salcedo-Sanz, 2021. "Optimal Generation Scheduling in Hydro-Power Plants with the Coral Reefs Optimization Algorithm," Energies, MDPI, vol. 14(9), pages 1-24, April.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:9:p:2443-:d:543058
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    References listed on IDEAS

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    4. Carolina Marcelino & Manuel Baumann & Leonel Carvalho & Nelson Chibeles-Martins & Marcel Weil & Paulo Almeida & Elizabeth Wanner, 2020. "A combined optimisation and decision-making approach for battery-supported HMGS," Journal of the Operational Research Society, Taylor & Francis Journals, vol. 71(5), pages 762-774, May.
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    Cited by:

    1. Carolina G. Marcelino & João V. C. Avancini & Carla A. D. M. Delgado & Elizabeth F. Wanner & Silvia Jiménez-Fernández & Sancho Salcedo-Sanz, 2021. "Dynamic Electric Dispatch for Wind Power Plants: A New Automatic Controller System Using Evolutionary Algorithms," Sustainability, MDPI, vol. 13(21), pages 1-20, October.

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