Abstract
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 language | English (US) |
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Title of host publication | Computer Aided Chemical Engineering |
Publisher | Elsevier B.V. |
Pages | 1219-1224 |
Number of pages | 6 |
DOIs | |
State | Published - Jan 2022 |
Externally published | Yes |
Publication series
Name | Computer Aided Chemical Engineering |
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Volume | 49 |
ISSN (Print) | 1570-7946 |
Bibliographical note
Publisher Copyright:© 2022 Elsevier B.V.
Keywords
- endogenous uncertainty
- stochastic optimization
- technology learning