AI Chat Paper
Note: Please note that the following content is generated by AMiner AI. SciOpen does not take any responsibility related to this content.
{{lang === 'zh_CN' ? '文章概述' : 'Summary'}}
{{lang === 'en_US' ? '中' : 'Eng'}}
Chat more with AI
Home iEnergy Article
View PDF
Collect
Submit Manuscript AI Chat Paper
Show Outline
Outline
Show full outline
Hide outline
Outline
Show full outline
Hide outline
Article | Open Access

Impact of climate simulation resolutions on future energy system reliability assessment: A Texas case study

Xiangtian Zheng1Le Xie1,2( )Kiyeob Lee1Dan Fu3Jiahan Wu1Ping Chang3
Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843, USA
Texas A&M Energy Institute, Texas A&M University, College Station, TX 77843, USA
Department of Oceangraphy, Texas A&M University, College Station, TX 77843, USA
Show Author Information

Abstract

The reliability of energy systems is strongly influenced by the prevailing climate conditions. With the increasing prevalence of renewable energy sources, the interdependence between energy and climate systems has become even stronger. This study examines the impact of different spatial resolutions in climate modeling on energy grid reliability assessment, with the Texas interconnection between 2033 and 2043 serving as a pilot case study. Our preliminary findings indicate that while low-resolution climate simulations can provide a rough estimate of system reliability, high-resolution simulations can provide more informative assessment of low-adequacy extreme events. Furthermore, both high- and low-resolution assessments suggest the need to prepare for severe blackout events in winter due to extremely low temperatures.

References

[1]

Wu, D., Zheng, X., Xu, Y., Olsen, D., Xia, B., Singh, C., Xie, L. (2021). An open-source extendable model and corrective measure assessment of the 2021 Texas power outage. Advances in Applied Energy, 4: 100056.

[2]

Auffhammer, M., Baylis, P., Hausman, C. H. (2017). Climate change is projected to have severe impacts on the frequency and intensity of peak electricity demand across the United States. Proceedings of the National Academy of Sciences, 114: 1886–1891.

[3]

Panteli, M., Mancarella, P. (2015). Influence of extreme weather and climate change on the resilience of power systems: Impacts and possible mitigation strategies. Electric Power Systems Research, 127: 259–270.

[4]

van Ruijven, B. J., De Cian, E., Sue Wing, I. (2019). Amplification of future energy demand growth due to climate change. Nature Communications, 10: 2762.

[5]

Pryor, S. C., Barthelmie, R. J., Bukovsky, M. S., Leung, L. R., Sakaguchi, K. (2020). Climate change impacts on wind power generation. Nature Reviews Earth & Environment, 1: 627–643.

[6]

Yalew, S. G., van Vliet, M. T. H., Gernaat, D. E. H. J., Ludwig, F., Miara, A., Park, C., Byers, E., De Cian, E., Piontek, F., Iyer, G., et al. (2020). Impacts of climate change on energy systems in global and regional scenarios. Nature Energy, 5: 794–802.

[7]

Perera, A. T. D., Nik, V. M., Chen, D., Scartezzini, J. L., Hong, T. (2020). Quantifying the impacts of climate change and extreme climate events on energy systems. Nature Energy, 5: 150–159.

[8]

Tong, D., Farnham, D. J., Duan, L., Zhang, Q., Lewis, N. S., Caldeira, K., Davis, S. J. (2021). Geophysical constraints on the reliability of solar and wind power worldwide. Nature Communications, 12: 6146.

[9]

Zeyringer, M., Price, J., Fais, B., Li, P.H., Sharp, E. (2018). Designing low-carbon power systems for Great Britain in 2050 that are robust to the spatiotemporal and inter-annual variability of weather. Nature Energy, 3: 395–403.

[10]

Perera, A. T. D., Javanroodi, K., Mauree, D., Nik, V. M., Florio, P., Hong, T., Chen, D. (2023). Challenges resulting from urban density and climate change for the EU energy transition. Nature Energy, 8: 397–412.

[11]

Chang, P., Zhang, S., Danabasoglu, G., Yeager, S. G., Fu, H., Wang, H., Castruccio, F. S., Chen, Y., Edwards, J., Fu, D., et al. (2020). An unprecedented set of high-resolution earth system simulations for understanding multiscale interactions in climate variability and change. Journal of Advances in Modeling Earth Systems, 12: e2020MS002298.

[12]

Palmer, T. N. (2019). Stochastic weather and climate models. Nature Reviews Physics, 1: 463–471.

[13]

Kashinath, K., Mustafa, M., Albert, A., Wu, J. L., Jiang, C., Esmaeilzadeh, S., Azizzadenesheli, K., Wang, R., Chattopadhyay, A., Singh, A., et al. (2021). Physics-informed machine learning: Case studies for weather and climate modelling. Philosophical Transactions Series A, Mathematical, Physical, and Engineering Sciences, 379: 20200093.

[14]

Ragone, F., Wouters, J., Bouchet, F. (2018). Computation of extreme heat waves in climate models using a large deviation algorithm. Proceedings of the National Academy of Sciences, 115: 24–29.

[15]
IPCC (2022). Climate Change 2022: Impacts, Adaptation, and Vulnerability. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (Pörtner, H. O., Roberts, D. C., Tignor, M., et al. Eds.). Cambridge, UK and New York: Cambridge University Press.
[16]

Craig, M. T., Wohland, J., Stoop, L. P., Kies, A., Pickering, B., Bloomfield, H. C., Browell, J., De Felice, M., Dent, C. J., Deroubaix, A., et al. (2022). Overcoming the disconnect between energy system and climate modeling. Joule, 6: 1405–1417.

[17]
iHESP (2023). Datasets. Available at https://ihesp.github.io/archive/products/ds_archive/ Datasets.html#global-datasets. Accessed on April 18, 2023.
[18]
Energy Information Administration (2023). Form EIA-860 detailed data with previous form data (EIA-860A/860B). Available at https://www.eia.gov/electricity/data/eia860/. Accessed on April 17, 2023.
[19]

Birchfield, A. B., Xu, T., Gegner, K. M., Shetye, K. S., Overbye, T. J. (2016). Grid structural characteristics as validation criteria for synthetic networks. IEEE Transactions on Power Systems, 32: 3258–3265.

[20]
Iowa State University of Science and Technology (2023). Download ASOS 1 minute interval data. Available at https://mesonet.agron.iastate.edu/request/asos/1min.phtml. Accessed on April 18, 2023.
[21]
Electric Reliability Council of Texas (2023). Consumer service portal—ERCOT public portal. Available at https://www.publicportal.ercot.com/csp. Accessed on April 18, 2023.
[22]
Electric Reliability Council of Texas (2023). 2023 ERCOT system planning long-term hourly peak demand and energy forecast. Available at https://www.ercot.com/files/docs/2023/01/18/2023-LTLF-Report.pdf, 2023. Accessed on April 18, 2023.
iEnergy
Pages 222-230
Cite this article:
Zheng X, Xie L, Lee K, et al. Impact of climate simulation resolutions on future energy system reliability assessment: A Texas case study. iEnergy, 2023, 2(3): 222-230. https://doi.org/10.23919/IEN.2023.0014

201

Views

19

Downloads

3

Crossref

0

Scopus

Altmetrics

Received: 05 May 2023
Revised: 21 June 2023
Accepted: 11 July 2023
Published: 09 August 2023
© The author(s) 2023.

This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

Return