9.4 Quantifying Time-Evolving Risk to Electricity Generation Using Climate−Model Statistics: Sensitivity of Business Decisions to Probability Information

Thursday, 11 January 2018: 9:15 AM
Room 15 (ACC) (Austin, Texas)
Terence R. Thompson, Logistics Management Institute, Tysons, VA

We estimate the impacts of changes in surface air temperature and precipitation on power-plant cooling-water temperature and electricity generation. We link this to localized climate-model statistical projections to calculate generation-capacity risk and determine how this risk changes quantitatively over time from a specified historical period to 2100. We then apply this technique to specific generation facilities in order to compare risk magnitudes and timelines quantitatively. Business decisions driven by localized statistics are compared using two different sources: model-weighted ensembles of LOCA data[1] and probability-weighted county-level ensembles[2]. This helps determine the degree to which business impacts and decisions are sensitive to variations in the underlying probability information.

[1] Terence R. Thompson, Kenneth E. Kunkel, Laura E. Stevens, David R. Easterling, James C. Biard, and Liqiang Sun, “Localized Trend Analysis of Multi-Model Extremes Metrics for the Fourth National Climate Assessment”, American Meteorological Society (AMS) Applied Climatology Conference, June 2017.
[2] D. Rasmussen, M. Meinshausen, and R. Kopp, “Probability-Weighted Ensembles of U.S. County-Level Climate Projections for Climate Risk Analysis”, J. Applied Meteorology and Climatology, 2016.
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