IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i9p2443-d543058.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/9/2443/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/14/9/2443/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Justel, Ana & Peña, Daniel & Zamar, Rubén, 1997. "A multivariate Kolmogorov-Smirnov test of goodness of fit," Statistics & Probability Letters, Elsevier, vol. 35(3), pages 251-259, October.
    2. P. M. R. Bento & S. J. P. S. Mariano & M. R. A. Calado & L. A. F. M. Ferreira, 2020. "A Novel Lagrangian Multiplier Update Algorithm for Short-Term Hydro-Thermal Coordination," Energies, MDPI, vol. 13(24), pages 1-19, December.
    3. 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.
    4. Bartosz Ceran & Jakub Jurasz & Robert Wróblewski & Adam Guderski & Daria Złotecka & Łukasz Kaźmierczak, 2020. "Impact of the Minimum Head on Low-Head Hydropower Plants Energy Production and Profitability," Energies, MDPI, vol. 13(24), pages 1-21, December.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    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.
    2. Carole Bernard & Oleg Bondarenko & Steven Vanduffel, 2021. "A model-free approach to multivariate option pricing," Review of Derivatives Research, Springer, vol. 24(2), pages 135-155, July.
    3. Jie Shi & Arno P. J. M. Siebes & Siamak Mehrkanoon, 2023. "TransCORALNet: A Two-Stream Transformer CORAL Networks for Supply Chain Credit Assessment Cold Start," Papers 2311.18749, arXiv.org.
    4. Squalli, Jay, 2017. "Renewable energy, coal as a baseload power source, and greenhouse gas emissions: Evidence from U.S. state-level data," Energy, Elsevier, vol. 127(C), pages 479-488.
    5. Chiragiev, Arthur & Landsman, Zinoviy, 2009. "Multivariate flexible Pareto model: Dependency structure, properties and characterizations," Statistics & Probability Letters, Elsevier, vol. 79(16), pages 1733-1743, August.
    6. Torri, Gabriele & Giacometti, Rosella & Paterlini, Sandra, 2018. "Robust and sparse banking network estimation," European Journal of Operational Research, Elsevier, vol. 270(1), pages 51-65.
    7. Yue, Zenghui & Xu, Haiyun & Yuan, Guoting & Pang, Hongshen, 2019. "Modeling study of knowledge diffusion in scientific collaboration networks based on differential dynamics: A case study in graphene field," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 524(C), pages 375-391.
    8. Shuo Huang & Xinyu Wu & Yiyang Wu & Zheng Zhang, 2023. "Mid-Term Optimal Scheduling of Low-Head Cascaded Hydropower Stations Considering Inflow Unevenness," Energies, MDPI, vol. 16(17), pages 1-13, September.
    9. Kamali Saraji, Mahyar & Aliasgari, Elahe & Streimikiene, Dalia, 2023. "Assessment of the challenges to renewable energy technologies adoption in rural areas: A Fermatean CRITIC-VIKOR approach," Technological Forecasting and Social Change, Elsevier, vol. 189(C).
    10. Langrené, Nicolas & Warin, Xavier, 2021. "Fast multivariate empirical cumulative distribution function with connection to kernel density estimation," Computational Statistics & Data Analysis, Elsevier, vol. 162(C).
    11. Jakub Jurasz & Jerzy Mikulik & Paweł B. Dąbek & Mohammed Guezgouz & Bartosz Kaźmierczak, 2021. "Complementarity and ‘Resource Droughts’ of Solar and Wind Energy in Poland: An ERA5-Based Analysis," Energies, MDPI, vol. 14(4), pages 1-24, February.
    12. Cheng, Qixiu & Lin, Yuqian & Zhou, Xuesong (Simon) & Liu, Zhiyuan, 2024. "Analytical formulation for explaining the variations in traffic states: A fundamental diagram modeling perspective with stochastic parameters," European Journal of Operational Research, Elsevier, vol. 312(1), pages 182-197.
    13. R. N. Rattihalli, 2023. "A Class of Multivariate Power Skew Symmetric Distributions: Properties and Inference for the Power-Parameter," Sankhya A: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 85(2), pages 1356-1393, August.
    14. Zuliqar Ali & Ijaz Hussain & Muhammad Faisal & Hafiza Mamona Nazir & Mitwali Abd-el Moemen & Tajammal Hussain & Sadaf Shamsuddin, 2017. "A Novel Multi-Scalar Drought Index for Monitoring Drought: the Standardized Precipitation Temperature Index," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(15), pages 4957-4969, December.
    15. Jean-David Fermanian, 2003. "Goodness of Fit Tests for Copulas," Working Papers 2003-34, Center for Research in Economics and Statistics.
    16. Abul Kalam Azad & Mohammad Golam Rasul & Talal Yusaf, 2014. "Statistical Diagnosis of the Best Weibull Methods for Wind Power Assessment for Agricultural Applications," Energies, MDPI, vol. 7(5), pages 1-30, May.
    17. Audrius Kabašinskas & Leonidas Sakalauskas & Ingrida Vaičiulytė, 2021. "An Analytical EM Algorithm for Sub-Gaussian Vectors," Mathematics, MDPI, vol. 9(9), pages 1-20, April.
    18. Wang, Rui & Purshouse, Robin C. & Fleming, Peter J., 2015. "Preference-inspired co-evolutionary algorithms using weight vectors," European Journal of Operational Research, Elsevier, vol. 243(2), pages 423-441.
    19. Petrie, Adam, 2016. "Graph-theoretic multisample tests of equality in distribution for high dimensional data," Computational Statistics & Data Analysis, Elsevier, vol. 96(C), pages 145-158.
    20. Katarína Remeňová & Jakub Kintler & Nadežda Jankelová, 2020. "The General Concept of the Revenue Model for Sustainability Growth," Sustainability, MDPI, vol. 12(16), pages 1-12, August.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:14:y:2021:i:9:p:2443-:d:543058. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.