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Stability Metric Based on Sensitivity Analysis Applied to Electrical Repowering System

Author

Listed:
  • João R. B. Paiva

    (Studies and Researches in Science and Technology Group (GCITE), Federal Institute of Goias (IFG), Goiania 74130-012, GO, Brazil
    Electrical, Mechanical and Computer Engineering School (EMC), Federal University of Goias (UFG), Goiania 74605-010, GO, Brazil)

  • Alana S. Magalhães

    (Studies and Researches in Science and Technology Group (GCITE), Federal Institute of Goias (IFG), Goiania 74130-012, GO, Brazil
    Electrical, Mechanical and Computer Engineering School (EMC), Federal University of Goias (UFG), Goiania 74605-010, GO, Brazil)

  • Pedro H. F. Moraes

    (Department of Electrical Engineering (DEE), University of Brasilia (UnB), Brasilia 70910-900, DF, Brazil)

  • Júnio S. Bulhões

    (Studies and Researches in Science and Technology Group (GCITE), Federal Institute of Goias (IFG), Goiania 74130-012, GO, Brazil
    Electrical, Mechanical and Computer Engineering School (EMC), Federal University of Goias (UFG), Goiania 74605-010, GO, Brazil)

  • Wesley P. Calixto

    (Studies and Researches in Science and Technology Group (GCITE), Federal Institute of Goias (IFG), Goiania 74130-012, GO, Brazil
    Electrical, Mechanical and Computer Engineering School (EMC), Federal University of Goias (UFG), Goiania 74605-010, GO, Brazil)

Abstract

Stability metrics are used to quantify a system’s ability to maintain equilibrium under disturbances. We did not identify the proposition of a stability metric using sensitivity analysis within the literature. This work proposes a system stability metric and its application to an electrical repowering system. The methodology for applying the proposed metric comprises: (i) system parameters sensitivity analysis and spider diagram construction, (ii) determining the array containing the line segments inclination angles of each spider diagram curve, and (iii) stability calculation using the array mean and maximum inclination value of a line segment. After simulating the model built for the electrical repowering system and applying the methodology, we obtain results regarding the sensitivity indices and stability values of system inputs relative to their outputs, considering the original system and with reduced parameters. Using the stability study, it was possible to determine different stability categories for the system parameters, which indicates the need for different analysis levels.

Suggested Citation

  • João R. B. Paiva & Alana S. Magalhães & Pedro H. F. Moraes & Júnio S. Bulhões & Wesley P. Calixto, 2021. "Stability Metric Based on Sensitivity Analysis Applied to Electrical Repowering System," Energies, MDPI, vol. 14(22), pages 1-21, November.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:22:p:7824-:d:685323
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    References listed on IDEAS

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    1. Escosa, Jesús M. & Romeo, Luis M., 2009. "Optimizing CO2 avoided cost by means of repowering," Applied Energy, Elsevier, vol. 86(11), pages 2351-2358, November.
    2. G. Dosi & M. C. Pereira & M. E. Virgillito, 2018. "On the robustness of the fat-tailed distribution of firm growth rates: a global sensitivity analysis," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 13(1), pages 173-193, April.
    3. H. Christopher Frey & Sumeet R. Patil, 2002. "Identification and Review of Sensitivity Analysis Methods," Risk Analysis, John Wiley & Sons, vol. 22(3), pages 553-578, June.
    4. Alan H. F. Silva & Alana S. Magalhaes & Junio S. Bulhoes & Gabriel A. Wainer & Gevanne P. Furriel & Wesley P. Calixto, 2021. "Parametric Regression Applied for Determination of Electrical Parameters of Synchronous and Induction Generators Operating in Parallel on the Electrical Energy Repowering System," Energies, MDPI, vol. 14(13), pages 1-21, June.
    5. D. Gareth Thomas & David S. Bywaters, 2021. "System Stability and Conclusions," Springer Books, in: The Creators of Inside Money, edition 2, chapter 0, pages 211-215, Springer.
    6. D. Gareth Thomas & David S. Bywaters, 2021. "The Creators of Inside Money," Springer Books, Springer, edition 2, number 978-3-030-70366-0, September.
    7. Sanajaoba Singh, Sarangthem & Fernandez, Eugene, 2018. "Modeling, size optimization and sensitivity analysis of a remote hybrid renewable energy system," Energy, Elsevier, vol. 143(C), pages 719-731.
    8. Mavromatidis, Georgios & Orehounig, Kristina & Carmeliet, Jan, 2018. "Uncertainty and global sensitivity analysis for the optimal design of distributed energy systems," Applied Energy, Elsevier, vol. 214(C), pages 219-238.
    9. Borgonovo, Emanuele & Plischke, Elmar, 2016. "Sensitivity analysis: A review of recent advances," European Journal of Operational Research, Elsevier, vol. 248(3), pages 869-887.
    10. Viviane M. Gomes & Joao R. B. Paiva & Marcio R. C. Reis & Gabriel A. Wainer & Wesley P. Calixto, 2019. "Mechanism for Measuring System Complexity Applying Sensitivity Analysis," Complexity, Hindawi, vol. 2019, pages 1-12, April.
    11. Helton, J.C. & Johnson, J.D. & Sallaberry, C.J. & Storlie, C.B., 2006. "Survey of sampling-based methods for uncertainty and sensitivity analysis," Reliability Engineering and System Safety, Elsevier, vol. 91(10), pages 1175-1209.
    12. Nam Ho Kim & Haoyu Wang & Nestor V. Queipo, 2006. "Adaptive reduction of random variables using global sensitivity in reliability-based optimisation," International Journal of Reliability and Safety, Inderscience Enterprises Ltd, vol. 1(1/2), pages 102-119.
    13. Bogumił Kamiński & Michał Jakubczyk & Przemysław Szufel, 2018. "A framework for sensitivity analysis of decision trees," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 26(1), pages 135-159, March.
    14. Dong Van Dao & Hojjat Adeli & Hai-Bang Ly & Lu Minh Le & Vuong Minh Le & Tien-Thinh Le & Binh Thai Pham, 2020. "A Sensitivity and Robustness Analysis of GPR and ANN for High-Performance Concrete Compressive Strength Prediction Using a Monte Carlo Simulation," Sustainability, MDPI, vol. 12(3), pages 1-22, January.
    15. Zhang, Taiping & Stackhouse, Paul W. & Macpherson, Bradley & Mikovitz, J. Colleen, 2021. "A solar azimuth formula that renders circumstantial treatment unnecessary without compromising mathematical rigor: Mathematical setup, application and extension of a formula based on the subsolar poin," Renewable Energy, Elsevier, vol. 172(C), pages 1333-1340.
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