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Impacts of renewable energy resources on the weather vulnerability of power systems

Nature Energy volume 9, pages 1407–1414 (2024)Cite this article

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Abstract

The high penetration of weather-dependent renewable energy sources (WD-RESs) such as wind and solar has raised concerns about the security of electric power systems during abnormal weather conditions. The role of RESs has been discussed in worldwide blackout events, yet remains controversial. In this study, we find that although WD-RESs are non-dispatchable and weather sensitive, blackout intensities and extreme weather vulnerability are mitigated in high-penetration WD-RES grids. The causal effects of WD-RESs on blackouts generally decrease in high-penetration WD-RES power systems, and WD-RESs are not mainly responsible for the occurrence of blackouts in extreme weather conditions. The results of our research contribute to the debate on RES integration and power system security, offer a guide for the study of power system resilience and provide a reference for the ambitious high-penetration RES goals of the future.

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Fig. 1: Probabilities and regression functions of different blackout intensities at different WD-RES penetration levels.
Fig. 2: Probabilities that a power grid with higher-penetration WD-RES has mitigated intensity.
Fig. 3: Rarity probability of weather-induced and non-weather-induced events.
Fig. 4: Rarity probabilities of weather-induced events with different WD-RES penetration levels.

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Data availability

The raw data of blackout events are available from DOE form OE-417 (ref. 46). Weather conditions come from the National Renewable Energy Laboratory (NREL) National Solar Radiation Database (NSRDB)34. Weather-dependent renewable energy penetration levels come from the US EIA 923 state-level generation and fuel consumption data30). The processed combined datasets described in Supplementary Note 1 are available in .mat form via Figshare at https://doi.org/10.6084/m9.figshare.25628700 (ref. 47). Source data are provided with this paper.

Code availability

Blackout data were pre-processed using Matlab 2019b. The statistical analysis and deep clustering were performed with Matlab 2019b correlation command and the Curve Fitting Tool, and Python 3.7, Keras 2.4.3 and Scikit-learn 0.24.2. All the tools mentioned above are open source and can be accessed by the public. Codes for statistical analysis, causal inference and deep clustering are available from the author upon reasonable request.

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Acknowledgements

We would like to thank the funding support in part from the US National Science Foundation (NSF) grant ECCS-1809458 and in part from the CURENT research centre which is a US NSF and Department of Energy Engineering Research Center funded under NSF grant EEC-1041877. In addition, J.Z. would like to thank the Alexander von Humboldt Foundation and the host professor C. Rehtanz during September 2022–June 2023, as their unconditional support ensures the continuation of this research work.

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Authors and Affiliations

  1. Department of Electrical Engineering and Computer Science, The University of Tennessee, Knoxville, TN, USA

    Jin Zhao, Fangxing Li & Qiwei Zhang

  2. Department of Electronic and Electrical Engineering, School of Engineering, Trinity College Dublin, Dublin, Ireland

    Jin Zhao

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Contributions

J.Z. and F.L. conceived the idea. J.Z. designed the study and wrote the initial draft. J.Z. and F.L. revised the article. J.Z. collected and analysed the data. Q.Z. validated the data. F.L. supervised the overall study.

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Correspondence to Fangxing Li.

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The authors declare no competing interests.

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Nature Energy thanks Russell McKenna, Wei Sun and Dirk Witthaut for their contribution to the peer review of this work.

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Supplementary Notes 1–8, Figs. 1–12 and Tables 1–14.

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Zhao, J., Li, F. & Zhang, Q. Impacts of renewable energy resources on the weather vulnerability of power systems. Nat Energy 9, 1407–1414 (2024). https://doi.org/10.1038/s41560-024-01652-1

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