Multistage Stochastic Unit Commitment Using Stochastic Dual Dynamic Integer Programming

Unit commitment (UC) is a key operational problem in power systems for the optimal schedule of daily generation commitment. Incorporating uncertainty in this already difficult mixed-integer optimization problem introduces significant computational challenges. Most existing stochastic UC models consi...

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Veröffentlicht in:IEEE transactions on power systems Jg. 34; H. 3; S. 1814 - 1823
Hauptverfasser: Zou, Jikai, Ahmed, Shabbir, Sun, Xu Andy
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.05.2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Adaptation models
Algorithms
Computation
Computational modeling
Dynamic programming
Heuristic algorithms
Integer programming
Linear programming
Mathematical models
Mathematical programming
Multistage
multistage stochastic integer programming
Optimization
Schedules
stochastic dual dynamic integer programming
Stochastic processes
Uncertainty
Unit commitment
ISSN:0885-8950, 1558-0679
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Zusammenfassung:Unit commitment (UC) is a key operational problem in power systems for the optimal schedule of daily generation commitment. Incorporating uncertainty in this already difficult mixed-integer optimization problem introduces significant computational challenges. Most existing stochastic UC models consider either a two-stage decision structure, where the commitment schedule for the entire planning horizon is decided before the uncertainty is realized, or a multistage stochastic programming model with relatively small scenario trees to ensure tractability. We propose a new type of decomposition algorithm, based on the recently proposed framework of stochastic dual dynamic integer programming (SDDiP), to solve the multistage stochastic unit commitment (MSUC) problem. We propose a variety of computational enhancements to SDDiP, and conduct systematic and extensive computational experiments to demonstrate that the proposed method is able to handle elaborate stochastic processes and can solve MSUCs with a huge number of scenarios that are impossible to handle by existing methods.
Bibliographie:ObjectType-Article-1
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ISSN:0885-8950
1558-0679
DOI:10.1109/TPWRS.2018.2880996