Capacity Planning for Sustainable Process Systems with Uncertain Endogenous Technology Learning

Tushar Rathi, Qi Zhang

Research output: Chapter in Book/Report/Conference proceedingChapter


The development and deployment of renewable technologies are key to achieving decar- bonization. Optimal capacity expansion requires complex decision making that accounts for future cost reduction with increased deployment, which is also termed technology learning. Having a perfect foresight over the technology cost reduction, however, is highly unlikely. This has motivated us to develop a capacity planning model that incorporates such uncertainty. To this end, we apply a multistage stochastic programming approach with endogenous uncertainty, which results in a mixed-integer linear programming (MILP) formulation. The proposed model is applied to a case study on power capacity expansion planning, highlighting the differences in expansion decisions for low- and high-learning scenarios, which indicates the importance of stochastic optimization.

Original languageEnglish (US)
Title of host publicationComputer Aided Chemical Engineering
PublisherElsevier B.V.
Number of pages6
StatePublished - Jan 2022

Publication series

NameComputer Aided Chemical Engineering
ISSN (Print)1570-7946

Bibliographical note

Publisher Copyright:
© 2022 Elsevier B.V.


  • endogenous uncertainty
  • stochastic optimization
  • technology learning


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