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)|
|Title of host publication||Computer Aided Chemical Engineering|
|Number of pages||6|
|State||Published - Jan 2022|
|Name||Computer Aided Chemical Engineering|
Bibliographical notePublisher Copyright:
© 2022 Elsevier B.V.
- endogenous uncertainty
- stochastic optimization
- technology learning