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Adaptive Protection System for Microgrids Based on a Robust Optimization Strategy

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  • Oscar Núñez-Mata

    (Energy Center, Department of Electrical Engineering, Faculty of Mathematical and Physical Sciences, University of Chile, 8370451 Santiago, Chile
    Electric Power and Energy Research Laboratory (EPER-Lab), School of Electrical Engineering, University of Costa Rica, 11501 San José, Costa Rica)

  • Rodrigo Palma-Behnke

    (Energy Center, Department of Electrical Engineering, Faculty of Mathematical and Physical Sciences, University of Chile, 8370451 Santiago, Chile)

  • Felipe Valencia

    (Energy Center, Department of Electrical Engineering, Faculty of Mathematical and Physical Sciences, University of Chile, 8370451 Santiago, Chile)

  • Patricio Mendoza-Araya

    (Energy Center, Department of Electrical Engineering, Faculty of Mathematical and Physical Sciences, University of Chile, 8370451 Santiago, Chile)

  • Guillermo Jiménez-Estévez

    (Energy Center, Department of Electrical Engineering, Faculty of Mathematical and Physical Sciences, University of Chile, 8370451 Santiago, Chile)

Abstract

The development of a proper protection system is essential for the secure and reliable operation of microgrids. In this paper, a novel adaptive protection system for microgrids is presented. The protection scheme is based on a protective device that includes two directional elements which are operating in an interleaved manner, namely overcurrent and undervoltage elements. The proposed protection scheme can be implemented in microprocessor-based relays. To define the settings of the protective device, a robust programming approach was proposed considering a finite set of fault scenarios. The scenarios are generated based on the predictions about the available energy and the demand. For each decision step, a robust optimization problem is solved online, which is based on forecasting with a confidence band to represent the uncertainty. The system is tested and compared using real data sets from an existing microgrid in northern Chile. To assess the performance of the proposed protection system, fault scenarios not considered in the optimization were taken into account. The results obtained show that the proposed protective device is able to manage those failure scenarios, as well as those included in the tuning of the settings. Practical considerations are also discussed.

Suggested Citation

  • Oscar Núñez-Mata & Rodrigo Palma-Behnke & Felipe Valencia & Patricio Mendoza-Araya & Guillermo Jiménez-Estévez, 2018. "Adaptive Protection System for Microgrids Based on a Robust Optimization Strategy," Energies, MDPI, vol. 11(2), pages 1-16, February.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:2:p:308-:d:129724
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    References listed on IDEAS

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    1. Craparo, Emily & Karatas, Mumtaz & Singham, Dashi I., 2017. "A robust optimization approach to hybrid microgrid operation using ensemble weather forecasts," Applied Energy, Elsevier, vol. 201(C), pages 135-147.
    2. Brearley, Belwin J. & Prabu, R. Raja, 2017. "A review on issues and approaches for microgrid protection," Renewable and Sustainable Energy Reviews, Elsevier, vol. 67(C), pages 988-997.
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    Citations

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    Cited by:

    1. Danny Espín-Sarzosa & Rodrigo Palma-Behnke & Oscar Núñez-Mata, 2020. "Energy Management Systems for Microgrids: Main Existing Trends in Centralized Control Architectures," Energies, MDPI, vol. 13(3), pages 1-32, January.
    2. Cristian Cepeda & Cesar Orozco-Henao & Winston Percybrooks & Juan Diego Pulgarín-Rivera & Oscar Danilo Montoya & Walter Gil-González & Juan Carlos Vélez, 2020. "Intelligent Fault Detection System for Microgrids," Energies, MDPI, vol. 13(5), pages 1-21, March.
    3. Hong Li & Xiaodan Wang & Jie Duan & Feifan Chen & Yajing Gao, 2018. "Locating Optimization of an Integrated Energy Supply Centre in a Typical New District Based on the Load Density," Energies, MDPI, vol. 11(4), pages 1-22, April.
    4. Abdul Wadood & Chang-Hwan Kim & Tahir Khurshiad & Saeid Gholami Farkoush & Sang-Bong Rhee, 2018. "Application of a Continuous Particle Swarm Optimization (CPSO) for the Optimal Coordination of Overcurrent Relays Considering a Penalty Method," Energies, MDPI, vol. 11(4), pages 1-20, April.
    5. Luis G. Cortés & J. Barbancho & D. F. Larios & J. D. Marin-Batista & A. F. Mohedano & C. Portilla & M. A. de la Rubia, 2022. "Full-Scale Digesters: An Online Model Parameter Identification Strategy," Energies, MDPI, vol. 15(20), pages 1-17, October.
    6. Longze Wang & Shucen Jiao & Yu Xie & Saif Mubaarak & Delong Zhang & Jinxin Liu & Siyu Jiang & Yan Zhang & Meicheng Li, 2021. "A Permissioned Blockchain-Based Energy Management System for Renewable Energy Microgrids," Sustainability, MDPI, vol. 13(3), pages 1-19, January.

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