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Electric Grid Reliability Implications for a Near-Zero Emissions Energy System
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  • Tyler Ruggles,
  • Ken Caldeira,
  • Lei Duan,
  • David Farnham,
  • Candise Henry,
  • Rebecca Peer
Tyler Ruggles
Carnegie Institution for Science Stanford

Corresponding Author:truggles@carnegiescience.edu

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Ken Caldeira
Carnegie Institution for Science
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Lei Duan
Carnegie Institution for Science Stanford
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David Farnham
Carnegie Institution for Science Stanford
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Candise Henry
Carnegie Institution for Science Stanford
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Rebecca Peer
Carnegie Institution for Science Stanford
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Abstract

Wind and solar energy technologies are, by their nature, variable. Variations in resource availability, based on weather patterns, occur on intra-day to inter-annual time scales. Many energy system models optimize over a single year of input weather and electricity demand data. Energy system planners need increased understanding of the variability in generation potential across multiple years and how this could impact model results. A system achieving 100% reliability modeled using Year A data will not necessarily achieve 100% reliability when applied to Year B data unless an overbuild safety margin is added. We demonstrate: 1) model results can vary significantly based on the year of data used, 2) adding wind and solar does not necessarily reduce the predictability of meeting reliability targets year-to-year and can improve predictability in many cases, and 3) we illustrate a method to derive safety margins to predictably meet 100% reliability year after year and find the least-cost option.