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A Hybrid Genetic Algorithm for Optimal Active Power Curtailment Considering Renewable Energy Generation

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Computational Science and Its Applications – ICCSA 2023 Workshops (ICCSA 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14105))

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Abstract

This paper analyzes the application of a population-based algorithm and its improvement in solving an optimal power flow problem. Simulations were performed on a 14-bus IEEE network modified to include renewable energy sources-based power plants: a wind park and two photovoltaic solar parks. In this scenario, the high penetration of intermittent energy sources in the grid makes it necessary to curtail active power during peak generation to maintain the balance between load and generation. However, European energy market regulations limit the annual curtailment of RES generators and penalize discriminatory curtailment actions between generators. This work exploits the minimization of transmission active loss while respecting its security constraints. Additionally, constraints were introduced in the optimal power flow problem to mitigate active power curtailment of the renewable source generators and to secure a non-discriminatory characteristic in curtailment decisions. The non-convex nature of the problem, intensified by the introduction of non-linear constraints, suggests the exploitation of heuristic algorithms to locate the optimal global solution. The obtained results demonstrate that a hybrid GA algorithm can improve convergence speed, and it is useful in determining the problem solution in cases where deterministic algorithms are unable to converge.

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Acknowledgments

The authors are grateful to the Foundation for Science and Technology (FCT, Portugal) for financial support through national funds FCT/MCTES (PIDDAC) to CeDRI (UIDB/05757/2020 and UIDP/05757/2020), SusTEC (LA/P/0007/2021). This work has been supported by NORTE-01-0247-FEDER-072615 EPO - Enline Power Optimization - The supra-grid optimization software.

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Authors and Affiliations

  1. Research Centre in Digitalization and Intelligent Robotics (CeDRI), Instituto Politécnico de Bragança, Bragança, Portugal

    André Pedroso, Yahia Amoura, Ana I. Pereira & Ângela Ferreira

  2. University of la Laguna, Santa Cruz de Tenerife, Spain

    Yahia Amoura

  3. Federal University of Technology - Paraná, Curitiba, Brazil

    André Pedroso

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  1. André Pedroso
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  3. Ana I. Pereira
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  4. Ângela Ferreira
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Correspondence to Ângela Ferreira .

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Editors and Affiliations

  1. University of Perugia, Perugia, Italy

    Osvaldo Gervasi

  2. University of Basilicata, Potenza, Italy

    Beniamino Murgante

  3. University of Minho, Braga, Portugal

    Ana Maria A. C. Rocha

  4. University of Cagliari, Cagliari, Italy

    Chiara Garau

  5. University of Basilicata, Potenza, Italy

    Francesco Scorza

  6. University of Massachusetts Medical School, Worcester, MA, USA

    Yeliz Karaca

  7. Polytechnic University of Bari, Bari, Italy

    Carmelo M. Torre

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Pedroso, A., Amoura, Y., Pereira, A.I., Ferreira, Â. (2023). A Hybrid Genetic Algorithm for Optimal Active Power Curtailment Considering Renewable Energy Generation. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2023 Workshops. ICCSA 2023. Lecture Notes in Computer Science, vol 14105. Springer, Cham. https://doi.org/10.1007/978-3-031-37108-0_31

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  • DOI: https://doi.org/10.1007/978-3-031-37108-0_31

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