@misc{21824,
  keywords = {Energy Markets and Policy Department, Energy Analysis and Environmental Impacts Division},
  author = {Etan Gumerman and Chris Marnay},
  title = {Learning and Cost Reductions for Generating Technologies in the National Energy Modeling System (NEMS)},
  abstract = {<p>This report describes how Learning-by-Doing (LBD) is implemented endogenously in the National Energy Modeling System (NEMS) for generating plants. LBD is experiential learning that correlates to a generating technology's capacity growth. The annual amount of Learning-by-Doing affects the annual overnight cost reduction. Currently, there is no straightforward way to integrate and make sense of all the diffuse information related to the endogenous learning calculation in NEMS. This paper organizes the relevant information from the NEMS documentation, source code, input files, and output files, in order to make the model's logic more accessible. The end results are shown in three ways: in a simple spreadsheet containing all the parameters related to endogenous learning; by an algorithm that traces how the parameters lead to cost reductions; and by examples showing how AEO 2004 forecasts the reduction of overnight costs for generating technologies over time.</p>},
  year = {2004},
  pages = {36},
  month = {01/2004},
  publisher = {LBNL},
  address = {Berkeley},
}