Quantifying Time-evolving Risk to Electricity Generation Using Climate-model Statistics

We present a simplified method to 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.  Additionally, we compare these detailed results to very fast screening techniques in which extremes indices serve as indicators of relative risk to generation capacity across hundreds to thousands of generation facilities.