Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Impacts of climate change on sub-regional electricity demand and distribution in the southern United States

Abstract

High average temperatures lead to high regional electricity demand for cooling buildings, and large populations generally require more aggregate electricity than smaller ones do. Thus, future global climate and population changes will present regional infrastructure challenges regarding changing electricity demand. However, without spatially explicit representation of this demand or the ways in which it might change at the neighbourhood scale, it is difficult to determine which electricity service areas are most vulnerable and will be most affected by these changes. Here we show that detailed projections of changing local electricity demand patterns are viable and important for adaptation planning at the urban level in a changing climate. Employing high-resolution and spatially explicit tools, we find that electricity demand increases caused by temperature rise have the greatest impact over the next 40 years in areas serving small populations, and that large population influx stresses any affected service area, especially during peak demand.

This is a preview of subscription content

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Figure 1: Per cent substation capacity used by customer service areas in the year 2011.
Figure 2: Per cent of substation capacity of average electricity demand by service area in 2030.
Figure 3: Per cent of substation capacity of peak electricity demand by service area in the 2050s.
Figure 4: Difference in demand percentage of capacity, 2052–2011.

References

  1. 1

    Wilbanks, T. J. & Fernandez, S. J. Climate Change and Infrastructure, Urban Systems, and Vulnerabilities Technical Report for the US Department of Energy in Support of the National Climate Assessment (Oak Ridge National Laboratory, 2012); http://www.esd.ornl.gov/eess/esd_fact_sheets/Infrastructure022912.pdf

    Google Scholar 

  2. 2

    McGrananhan, G., Balk, D. & Anderson, B. The rising tide: assessing the risks of climate change and human settlements in low elevation coastal zones. Environ. Urban 19, 17–37 (2007).

    Article  Google Scholar 

  3. 3

    Warner, K., Erhart, C., de Sherbinin, A., Adamo, S. B. & Chai-Onn, T. In Search of Shelter: Mapping the Effects of Climate Change on Human Migration and Displacement (United Nations University, CARE and CIESIN-Columbia Univ., 2009).

    Google Scholar 

  4. 4

    Benson, C. & Clay, E. Understanding the Economic and Financial Impact of Natural Disasters. The International Bank for Reconstruction and Development (The World Bank, 2004).

    Book  Google Scholar 

  5. 5

    Dilley, M., Chen, R. S., Deichmann, U., Lerner-Lam, A. L. & Arnold, M. Natural Disaster Hotspots: A Global Risk Analysis (The World Bank, 2005).

    Book  Google Scholar 

  6. 6

    Kopp, R. et al. The American Climate Prospectus: Economics Risks in the United States (Rhodium Group, 2014); http://climateprospectus.org/publications

  7. 7

    Hadley, S. W., Erickson, D. J. III & Hernandez, J. L. Responses of energy use to climate change: a climate modeling study. Geophys. Res. Lett. 33, L17703 (2006).

    Article  Google Scholar 

  8. 8

    Hidalgo, H. G., Dettinger, M. D. & Cayan, D. R. Downscaling with Constructed Analogues: Daily Precipitation and Temperature Fields Over the United States PIER Final Project Report CEC-500-2007-123 (California Energy Commission, 2008).

    Google Scholar 

  9. 9

    Batty, M. The size, scale, and shape of cities. Science 319, 769–771 (2008).

    Article  Google Scholar 

  10. 10

    Lowry, I. S. A Model of Metropolis Memorandum RM-4035-RC (The RAND Corporation, 1964); https://www.rand.org/content/dam/rand/pubs/research_memoranda/2006/RM4035.pdf

  11. 11

    Garin, R. A. A matrix formulation of the Lowry model for intrametropolitan activity location. J. Am. Inst. Plan. 32, 361–364 (1966).

    Article  Google Scholar 

  12. 12

    Clarke, K. C. & Leonard, J. G. Loose-coupling a cellular automation model and GIS: long-term urban growth prediction for San Francisco and Washington/Baltimore. Lect. Notes Comput. Sci. 12, 699–714 (1998).

    Google Scholar 

  13. 13

    LoadSEERTM Spatial Electric Expansion & Risk (Integral Analytics, 2010); http://www.integralanalytics.com/files/documents/related-documents/LoadSEER.pdf

  14. 14

    Auffhammer, M. & Aroonruengsawat, A. Hotspots of Climate-Driven Increases in Residential Electricity Demand: A simulation Exercise Based on Household Level Billing Data for California White Paper CEC-500-2012-021 (California Energy Commission, 2012); http://www.energy.ca.gov/2012publications/CEC-500-2012-021/CEC-500-2012-021.pdf

  15. 15

    IPCC Climate Change 2007: Impacts, Adaptation and Vulnerability (eds Parry, M. L., Canziani, O. F., Palutikof, J. P., van der Linden, P. J. & Hanson, C. E. ) 23–78 (Cambridge Univ. Press, 2007).

    Google Scholar 

  16. 16

    IPCC Climate Change 2013: The Physical Science Basis (eds Stocker, T. F. et al.) (Cambridge Univ. Press, 2013).

    Google Scholar 

  17. 17

    Emanuel, K. Increasing destructiveness of tropical cyclones over the past 30 years. Nature 436, 686–688 (2005).

    Article  Google Scholar 

  18. 18

    Emanuel, K. A. Downscaling CMIP5 climate models shows increased tropical cyclone activity over the 21st century. Proc. Natl Acad. Sci. USA 110, 12219–12224 (2013).

    Article  Google Scholar 

  19. 19

    Bhaduri, B. L., Bright, E., Coleman, P. & Urban, M. LandScan USA: a high resolution geospatial and temporal modeling approach for population distribution and dynamics. GeoJournal 69, 103–117 (2007).

    Article  Google Scholar 

  20. 20

    Cheriyadat, A., Bright, E. A., Bhaduri, B. L. & Potere, D. Mapping of settlements in high resolution satellite imagery using high performance computing. GeoJournal 69, 119–129 (2007).

    Article  Google Scholar 

  21. 21

    Young, B. S., Fernandez, S. J. & Omitaomu, O. A. Dynamic Modeling of Components on the Electric Grid Report ORNL/TM-0000/00 (Oak Ridge National Laboratory, 2009).

    Google Scholar 

  22. 22

    McKee, J. J., Rose, A. N., Bright, E., Huynh, T. & Bhaduri, B. L. A locally-adaptive, spatially-explicit projection of US population for 2030 and 2050. Proc. Natl Acad. Sci. USA 112, 1344–1349 (2015).

    Article  Google Scholar 

  23. 23

    Mays, G. T. et al. Application of Spatial Data Modeling and Geographical Information Systems (GIS) for Identification of Potential Siting Options for Various Electrical Generation Sources Technical Report ORNL/TM-2011/157/R1 (ORNL, 2012).

  24. 24

    Allen, M. R., Fernandez, S. J., Fu, J. S. & Walker, K. A. Electricity demand evolution driven by storm motivated population movement. J. Geogr. Nat. Disast. 4, 126 (2014).

    Article  Google Scholar 

  25. 25

    Keim, B. D., Muller, R. A. & Stone, G. W. Spatiotemporal patterns and return periods of tropical storm and hurricane strikes from Texas to Maine. J. Clim. 20, 3498–3509 (2007).

    Article  Google Scholar 

  26. 26

    Noy, I. The macroeconomic consequences of disasters. J. Dev. Econ. 88, 221–231 (2009).

    Article  Google Scholar 

  27. 27

    Toole, G. L. et al. Effects of climate change on California energy security. Int. Symp. Systems and Human Science LA-UR-06-0984 (America Analytics, 2006); http://almeriaanalytics.com/articles-and-white-paper-2

  28. 28

    United States Department of Agriculture (USDA) Plant Hardiness Zone Map (Agricultural Research Service, accessed 3 June 2016); http://www.almanac.com/content/plant-hardiness-zones

  29. 29

    Deschenes, O. & Greenstone, M. Climate change, mortality, and adaptation: evidence from annual fluctuations in weather in the US. Am. Econ. J. Appl. Econ. 3, 152–185 (2011).

    Article  Google Scholar 

  30. 30

    Gao, Y., Fu, J. S., Drake, J. B., Liu, Y. & Lamarque, J.-F. Projected changes of extreme weather events in the eastern United States based on a high resolution climate modeling system. Environ. Res. Lett. 7, 044025 (2012).

    Article  Google Scholar 

  31. 31

    Flato, G. et al. in Climate Change 2013: The Physical Science Basis (eds Stocker, T. F. et al.) 741–866 (IPCC, Cambridge Univ. Press, 2013).

    Google Scholar 

  32. 32

    Vrac, M. et al. Dynamical and statistical downscaling of the French Mediterranean climate: uncertainty assessment. Nat. Hazards Earth Syst. Sci. 12, 2769–2784 (2012).

    Article  Google Scholar 

  33. 33

    Trzaska, Y. & Schnarr, E. A Review of Downscaling Methods for Climate Change Projections (USAID, 2014).

    Google Scholar 

  34. 34

    Annual Energy Outlook (AEO): 2005–2040 (EIA, accessed 18 August 2015); http://www.eia.gov/forecasts/aeo/data.cfm

  35. 35

    State Energy Data System (SEDS): 1960–2012 (Complete) (EIA, accessed 18 August 2015); http://www.eia.gov/state/seds/seds-data-complete.cfm

  36. 36

    Demsar, U., Spatenkova, O. & Virrantaus, K. Identifying critical locations in a spatial network with graph theory. Transact. GIS 12, 61–82 (2008).

    Article  Google Scholar 

  37. 37

    Edenhofer, O. et al. (eds) Special Report on Renewable Energy Sources and Climate Change Mitigation. Prepared by Working Group III of the Intergovernmental Panel on Climate Change (Cambridge Univ. Press, 2011).

  38. 38

    O’Neill, B. C. et al. A new scenario framework for climate change research: the concept of shared socioeconomic pathways. Clim. Change 122, 387–400 (2014).

    Article  Google Scholar 

  39. 39

    SOI Tax Stats County-to-County Migration Data Files (Internal Revenue Service, accessed 18 August 2015); http://www.irs.gov/uac/SOI-Tax-Stats-County-to-County-Migration-Data-Files

  40. 40

    Omitaomu, O. A., Fernandez, S. J. & Bhaduri, B. L. Handbook of Emergency Response: A Human Factors and Systems Engineering Approach (eds Adedeji, B. B. & LeeAnn, R. ) (CRC Press, 2013).

    Google Scholar 

  41. 41

    Pielke, R. A. Jr et al. Normalized hurricane damage in the United States. Nat. Hazards Rev. 9, 29–42 (2008).

    Article  Google Scholar 

Download references

Acknowledgements

This manuscript has been authored by employees of UT-Battelle, under contract DE-AC05-00OR22725 with the US Department of Energy. The authors would also like to acknowledge the financial support for this research by the Integrated Assessment Research Program of the US Department of Energy’s Office of Science. We thank the Tennessee Valley Authority and Electric Reliability Council of Texas for their provision of power data to the project.

Author information

Affiliations

Authors

Contributions

M.R.A., S.J.F. and J.S.F. designed the study. M.R.A. collected the population, migration and electricity consumption data and performed the service area and per cent demand calculations. J.S.F. provided the climate and WRF-downscaled temperature data and analysis. M.M.O. acquired, archived, documented and analysed the substation capacity data. All participated in drafting, reviewing and revising the manuscript.

Corresponding author

Correspondence to Melissa R. Allen.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Supplementary information

Supplementary Information

Supplementary Figures 1–17, Supplementary Tables 1–2, Supplementary Note 1, Supplementary References. (PDF 7118 kb)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Allen, M., Fernandez, S., Fu, J. et al. Impacts of climate change on sub-regional electricity demand and distribution in the southern United States. Nat Energy 1, 16103 (2016). https://doi.org/10.1038/nenergy.2016.103

Download citation

Further reading

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing