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The effects of the high penetration of renewable energies on the reliability and vulnerability of interconnected electric power systems

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  • Beyza, Jesus
  • Yusta, Jose M.

Abstract

Renewable energy sources and cross-border electrical interconnections can significantly impact the security of the supply of power systems. This article jointly analyses the reliability and vulnerability of electrical networks to quantify systems' performance by increasing and decreasing renewable resources and the degree of coupling of electrical infrastructures. This comparison seeks to measure the influence of renewable generation and the impact of interconnection lines on the operational behaviour of systems under different types of contingencies or disturbances. The reliability assessment is performed using PLEXOS, and the vulnerability assessment is carried out with a network disintegration procedure implemented in MATLAB. Here, different statistical indices of the networks are measured. The procedures are applied sequentially in six case studies with different generation mixes and interconnection lines based on the well-known IEEE RTS-96 and IEEE RTS-GMLC test networks. From the analysed cases, the resulting tables and graphs obtained from the simulation are presented, and the joint impact from the two perspectives is compared. The results obtained show that renewable sources have a greater impact on reliability and that electrical interconnections impact both reliability and vulnerability. These conclusions highlight the importance of analysing the operational security of infrastructures taking into account both approaches simultaneously.

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  • Beyza, Jesus & Yusta, Jose M., 2021. "The effects of the high penetration of renewable energies on the reliability and vulnerability of interconnected electric power systems," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    • Handle: RePEc:eee:reensy:v:215:y:2021:i:c:s0951832021004002
    DOI: 10.1016/j.ress.2021.107881
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    References listed on IDEAS

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    5. Aizpurua, J.I. & Stewart, B.G. & McArthur, S.D.J. & Penalba, M. & Barrenetxea, M. & Muxika, E. & Ringwood, J.V., 2022. "Probabilistic forecasting informed failure prognostics framework for improved RUL prediction under uncertainty: A transformer case study," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
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    7. Siripha Junlakarn & Radhanon Diewvilai & Kulyos Audomvongseree, 2022. "Stochastic Modeling of Renewable Energy Sources for Capacity Credit Evaluation," Energies, MDPI, vol. 15(14), pages 1-27, July.

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