DocumentCode :
135920
Title :
Stochastic optimization formulations for reliability unit commitment runs
Author :
Kai Pan ; Yang Lu ; Yongpei Guan ; Watson, Jean-Paul
Author_Institution :
Dept. of Ind. & Syst. Eng., Univ. of Florida, Gainesville, FL, USA
fYear :
2014
fDate :
27-31 July 2014
Firstpage :
1
Lastpage :
5
Abstract :
To address the uncertainties caused by the penetration of intermittent renewable energy, most ISOs/RTOs perform day-ahead and look-ahead reliability unit commitment (RUC) runs, ensuring sufficient generation capacity available in real time to accommodate the uncertainties. Two-stage stochastic optimization models have been studied extensively to strengthen the RUC runs, while multi-stage stochastic optimization models were barely studied. In this paper, we investigate the unit commitment and economic dispatch decision differences generated by these two approaches considering the load uncertainties in the system. The stochasticity is represented by a set of scenarios for the two-stage model and a scenario tree for the multi-stage case.
Keywords :
power generation dispatch; power generation economics; power generation reliability; power generation scheduling; stochastic programming; trees (mathematics); ISO; RTO; day-ahead RUC runs; economic dispatch decision differences; generation capacity; intermittent renewable energy; load uncertainties; look-ahead reliability unit commitment runs; multistage case; scenario tree; stochastic optimization formulations; two-stage model; Biological system modeling; Computational modeling; Electricity; Generators; Load modeling; Stochastic processes; Uncertainty; Unit commitment; mixed-integer linear programming; multi-stage stochastic optimization; two-stage stochastic optimization;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
PES General Meeting | Conference & Exposition, 2014 IEEE
Conference_Location :
National Harbor, MD
Type :
conf
DOI :
10.1109/PESGM.2014.6939833
Filename :
6939833
Link To Document :
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