Partially Adaptive Stochastic Optimization for Electric Power Generation Expansion Planning

Jikai Zou (), Shabbir Ahmed () and Xu Andy Sun ()
Additional contact information
Jikai Zou: School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332
Shabbir Ahmed: School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332
Xu Andy Sun: School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332

INFORMS Journal on Computing, 2018, vol. 30, issue 2, 388-401

Abstract: Electric power generation expansion planning (GEP) is the problem of determining an optimal construction and generation plan of both new and existing electric power plants to meet future electricity demand. We consider a stochastic optimization approach for this capacity expansion problem under demand and fuel price uncertainty. In a two-stage stochastic optimization model for GEP, the capacity expansion plan for the entire planning horizon is decided prior to the uncertainty realized and hence allows no adaptivity to uncertainty evolution over time. In comparison, a multistage stochastic optimization model allows full adaptivity to the uncertainty evolution but is extremely difficult to solve. To reconcile the trade-off between adaptivity and tractability, we propose a partially adaptive stochastic mixed integer optimization model in which the capacity expansion plan is fully adaptive to the uncertainty evolution up to a certain period and follows the two-stage approach thereafter. Any solution to the partially adaptive model is feasible to the multistage model, and we provide analytical bounds on the quality of such a solution. We propose an efficient algorithm that solves a sequence of partially adaptive models, to recursively construct an approximate solution to the multistage problem. We identify sufficient conditions under which this algorithm recovers an optimal solution to the multistage problem. Finally, we conduct extensive test of our algorithm on a realistic GEP problem. Experiments show that, within a reasonable computation time limit, the proposed algorithm produces a significantly better solution than solving the multistage model directly.

Keywords: generation expansion planning; multistage stochastic optimization; approximation algorithm (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

Downloads: (external link)
https://doi.org/10.1287/ijoc.2017.0782 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:inm:orijoc:v:30:y:2018:i:2:p:388-401

Access Statistics for this article

More articles in INFORMS Journal on Computing from INFORMS Contact information at EDIRC.
Bibliographic data for series maintained by Chris Asher ().