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Generic Framework for the Optimal Implementation of Flexibility Mechanisms in Large-Scale Smart Grids

Author

Listed:
  • Alejandro J. del Real

    (Department of Systems and Automation, University of Seville, 41092 Seville, Spain)

  • Andrés Pastor

    (IDENER, 41300 Seville, Spain)

  • Jaime Durán

    (IDENER, 41300 Seville, Spain)

Abstract

This paper aims to provide the smart grid research community with an open and accessible general mathematical framework to develop and implement optimal flexibility mechanisms in large-scale network applications. The motivation of this paper is twofold. On the one hand, flexibility mechanisms are currently a hot topic of research, which is aimed to mitigate variation and uncertainty of electricity demand and supply in decentralised grids with a high aggregated share of renewables. On the other hand, a large part of such related research is performed by heuristic methods, which are generally inefficient (such methods do not guarantee optimality) and difficult to extrapolate for different use cases. Alternatively, this paper presents an MPC-based (model predictive control) framework explicitly including a generic flexibility mechanism, which is easy to particularise to specific strategies such as demand response, flexible production and energy efficiency services. The proposed framework is benchmarked with other non-optimal control configurations to better show the advantages it provides. The work of this paper is completed by the implementation of a generic use case, which aims to further clarify the use of the framework and, thus, to ease its adoption by other researchers in their specific flexibility mechanism applications.

Suggested Citation

  • Alejandro J. del Real & Andrés Pastor & Jaime Durán, 2021. "Generic Framework for the Optimal Implementation of Flexibility Mechanisms in Large-Scale Smart Grids," Energies, MDPI, vol. 14(23), pages 1-14, December.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:23:p:8063-:d:693434
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    References listed on IDEAS

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    1. Kaldellis, J.K. & Zafirakis, D., 2007. "Optimum energy storage techniques for the improvement of renewable energy sources-based electricity generation economic efficiency," Energy, Elsevier, vol. 32(12), pages 2295-2305.
    2. Fadaeenejad, M. & Saberian, A.M. & Fadaee, Mohd. & Radzi, M.A.M. & Hizam, H. & AbKadir, M.Z.A., 2014. "The present and future of smart power grid in developing countries," Renewable and Sustainable Energy Reviews, Elsevier, vol. 29(C), pages 828-834.
    3. Pedro Henrique Valderrama Bento da Silva & Eduardo Camponogara & Laio Oriel Seman & Gabriel Villarrubia González & Valderi Reis Quietinho Leithardt, 2020. "Decompositions for MPC of Linear Dynamic Systems with Activation Constraints," Energies, MDPI, vol. 13(21), pages 1-26, November.
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    1. Carlos Henrique Valério de Moraes & Jonas Lopes de Vilas Boas & Germano Lambert-Torres & Gilberto Capistrano Cunha de Andrade & Claudio Inácio de Almeida Costa, 2022. "Intelligent Power Distribution Restoration Based on a Multi-Objective Bacterial Foraging Optimization Algorithm," Energies, MDPI, vol. 15(4), pages 1-23, February.

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