EconPapers    
Economics at your fingertips  
 

Multistage Adaptive Robust Optimization for the Unit Commitment Problem

Álvaro Lorca (), X. Andy Sun (), Eugene Litvinov () and Tongxin Zheng ()
Additional contact information
Álvaro Lorca: H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332
X. Andy Sun: H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332
Eugene Litvinov: ISO New England, Holyoke, Massachusetts 01040
Tongxin Zheng: ISO New England, Holyoke, Massachusetts 01040

Operations Research, 2016, vol. 64, issue 1, 32-51

Abstract: The growing uncertainty associated with the increasing penetration of wind and solar power generation has presented new challenges to the operation of large-scale electric power systems. Motivated by these challenges, we present a multistage adaptive robust optimization model for the most critical daily operational problem of power systems, namely, the unit commitment (UC) problem, in the situation where nodal net electricity loads are uncertain. The proposed multistage robust UC model takes into account the time causality of the hourly unfolding of uncertainty in the power system operation process, which we show to be relevant when ramping capacities are limited and net loads present significant variability. To deal with large-scale systems, we explore the idea of simplified affine policies and develop a solution method based on constraint generation. Extensive computational experiments on the IEEE 118-bus test case and a real-world power system with 2,736 buses demonstrate that the proposed algorithm is effective in handling large-scale power systems and that the proposed multistage robust UC model can significantly outperform the deterministic UC and existing two-stage robust UC models in both operational cost and system reliability.

Keywords: electric energy systems; multistage robust optimization; affine policies; constraint generation (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (39)

Downloads: (external link)
http://dx.doi.org/10.1287/opre.2015.1456 (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:oropre:v:64:y:2016:i:1:p:32-51

Access Statistics for this article

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

 
Page updated 2025-03-19
Handle: RePEc:inm:oropre:v:64:y:2016:i:1:p:32-51