Uncertainty in Unit Commitment in Power Systems: A Review of Models, Methods, and Applications

Ying-Yi Hong and Gerard Francesco DG. Apolinario
Additional contact information
Ying-Yi Hong: Department of Electrical Engineering, Chung Yuan Christian University, Taoyuan 32023, Taiwan
Gerard Francesco DG. Apolinario: Department of Electrical Engineering, Chung Yuan Christian University, Taoyuan 32023, Taiwan

Energies, 2021, vol. 14, issue 20, 1-47

Abstract: The unit commitment problem (UCP) is one of the key and fundamental concerns in the operation, monitoring, and control of power systems. Uncertainty management in a UCP has been of great interest to both operators and researchers. The uncertainties that are considered in a UCP can be classified as technical (outages, forecast errors, and plugin electric vehicle (PEV) penetration), economic (electricity prices), and “epidemics, pandemics, and disasters” (techno-socio-economic). Various methods have been developed to model the uncertainties of these parameters, such as stochastic programming, probabilistic methods, chance-constrained programming (CCP), robust optimization, risk-based optimization, the hierarchical scheduling strategy, and information gap decision theory. This paper reviews methods of uncertainty management, parameter modeling, simulation tools, and test systems.

Keywords: chance-constrained programming; hierarchical scheduling strategy; information gap decision theory; probabilistic methods; risk-based optimization; robust optimization; stochastic programming; unit commitment problem; uncertainty (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

Downloads: (external link)
https://www.mdpi.com/1996-1073/14/20/6658/pdf (application/pdf)
https://www.mdpi.com/1996-1073/14/20/6658/ (text/html)

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:gam:jeners:v:14:y:2021:i:20:p:6658-:d:656355

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().