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Extending energy system modelling to include extreme weather risks and application to hurricane events in Puerto Rico

Nature Energy volume 6, pages 240–249 (2021)Cite this article

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Abstract

Energy system optimization models often incorporate climate change impacts to examine different energy futures and draw insights that inform policy. However, increased risk of extreme weather events from climate change has proven more difficult to model. Here, we present an energy system optimization model that incorporates hurricane risks by combining storm probabilities with infrastructure fragility curves, and demonstrate its utility in the context of Puerto Rico, an island territory of the United States that had its energy system severely damaged by Hurricane Maria in 2017. The model assesses the potential to change grid architecture, fuel mix and grid hardening measures considering hurricane impacts as well as climate mitigation policies. When hurricane trends are included, 2040 electricity cost projections increase by 32% based on historical hurricane frequencies and by 82% for increased hurricane frequencies. Transitioning to renewables and natural gas reduces costs and emissions independent of climate mitigation policies.

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Fig. 1: Stylized grid topologies reveal some of the choices in grid architecture and power generation facing planners.
Fig. 2: Interactions of electric grid planning options.
Fig. 3: Hurricane Maria revealed the vulnerability of Puerto Rico’s current electric grid.
Fig. 4: Overview of framework for grid planning with extreme weather.
Fig. 5: Technology fragility curves.
Fig. 6: Projections of electricity costs, emissions and activity for stochastic optimization including reconstruction after severe weather events.
Fig. 7: Projections of electricity costs and emissions for case-based simulations.

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Data availability

The input dataset is available for download with the code at https://doi.org/10.18130/V3/QB0NPX (ref. 68). All model inputs are summarized in Supplementary Notes 1–3. Source data are provided with this paper.

Code availability

To enable replication of our work, the model and analysis code are open source, and an archived version is available for download at https://doi.org/10.18130/V3/QB0NPX (ref. 68). This includes the Python package and all scripts used to instantiate Temoa, run the analyses and create the plots in this article, which are also available for download at https://github.com/EnergyModels/temoatools.

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Acknowledgements

Support for this work came from the University of Virginia Environmental Resilience Institute and the Rotating Machinery and Controls Laboratory. We acknowledge Research Computing (http://rc.virginia.edu) at the University of Virginia for providing computational resources and technical support that contributed to the results reported within this publication.

Author information

Authors and Affiliations

  1. Department of Engineering Systems and Environment, University of Virginia, Charlottesville, VA, USA

    Jeffrey A. Bennett, Claire N. Trevisan & Andres F. Clarens

  2. Department of Civil, Construction, and Environmental Engineering, North Carolina State University, Raleigh, NC, USA

    Joseph F. DeCarolis

  3. Department of Social Sciences, University of Puerto Rico Mayagüez, Mayagüez, PR, USA

    Cecilio Ortiz-García & Marla Pérez-Lugo

  4. McIntire School of Commerce and Frank Batten School of Leadership and Public Policy, University of Virginia, Charlottesville, VA, USA

    Bevin T. Etienne

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Contributions

J.A.B., C.N.T., J.F.D. and A.F.C. designed the research. J.A.B. and C.N.T. conducted the literature review and data collection. J.A.B. performed the analysis. J.A.B. and A.F.C. created the figures. J.F.D. supported model development and implementation. C.O.-G., M.P.-L. and B.T.E. provided feedback on the scenarios and framing. J.A.B., J.F.D. and A.F.C. wrote the manuscript.

Corresponding author

Correspondence to Andres F. Clarens.

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The authors declare no competing interests.

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Peer review information Nature Energy thanks Christian Otto and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Supplementary information

Supplementary Information

Supplementary Notes 1–9, Tables 1–16, Figs. 1–5 and references.

Source data

Source Data Fig. 5

Fragility curve outputs as a function of wind speed as calculated in Python.

Source Data Fig. 6

Raw data for three subplots. Columns 1 and 2 identify subplot and quantity being plotted. Subplots a and b are box plots that use the raw data provided; subplot c creates line plots using the summarized data (minimum, mean, maximum) for the cases included.

Source Data Fig. 7

Statistical data (minimum, mean, maximum) for cost of electricity and emissions by case.

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Bennett, J.A., Trevisan, C.N., DeCarolis, J.F. et al. Extending energy system modelling to include extreme weather risks and application to hurricane events in Puerto Rico. Nat Energy 6, 240–249 (2021). https://doi.org/10.1038/s41560-020-00758-6

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