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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Abstract	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.
AbstractList	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.
Author	Zou, Jikai Sun, Xu Andy Ahmed, Shabbir
Author_xml	– sequence: 1 givenname: Jikai surname: Zou fullname: Zou, Jikai email: jikai.zou@gatech.edu organization: Department of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA, USA – sequence: 2 givenname: Shabbir surname: Ahmed fullname: Ahmed, Shabbir email: shabbir.ahmed@isye.gatech.edu organization: Department of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA, USA – sequence: 3 givenname: Xu Andy orcidid: 0000-0003-3917-9418 surname: Sun fullname: Sun, Xu Andy email: andy.sun@isye.gatech.edu organization: Department of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA, USA
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SubjectTerms	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
Title	Multistage Stochastic Unit Commitment Using Stochastic Dual Dynamic Integer Programming
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