Stochastic Unit Commitment via Progressive Hedging - Extensive Analysis of Solution Methods

Christos Ordoudis, Pierre Pinson, Marco Zugno, Juan Miguel Morales González

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

Abstract

Owing to the massive deployment of renewable power production units over the last couple of decades, the use of stochastic optimization methods to solve the unit commitment problem has gained increasing attention. Solving stochastic unit commitment problems in large-scale power systems requires high computational power, as stochastic models are dramatically more complex than their deterministic counterparts. This paper provides new insight into the potential of Progressive Hedging to decrease the solution time of the stochastic unit commitment problem with a relatively small trade-off in terms of the suboptimality of the solution. Computational studies show that the run-time is at most half of what is needed to solve the original extensive formulation of the problem, when more than ten wind power scenarios are utilized. These studies demonstrate great potential for solving real-world stochastic unit commitment problems using the Progressive Hedging algorithm.
Original languageEnglish
Title of host publicationProceedings of the IEEE PowerTech Conference 2015
Number of pages6
PublisherIEEE
Publication date2015
ISBN (Print)9781479976935
DOIs
Publication statusPublished - 2015
Event2015 IEEE PowerTech - Eindhoven, Netherlands
Duration: 29 Jun 20152 Jul 2015

Conference

Conference2015 IEEE PowerTech
CountryNetherlands
CityEindhoven
Period29/06/201502/07/2015

Keywords

  • Electricity market operations
  • Progressive hedging
  • Stochastic unit commitment
  • Wind power

Cite this

Ordoudis, C., Pinson, P., Zugno, M., & Morales González, J. M. (2015). Stochastic Unit Commitment via Progressive Hedging - Extensive Analysis of Solution Methods. In Proceedings of the IEEE PowerTech Conference 2015 IEEE. https://doi.org/10.1109/PTC.2015.7232629
Ordoudis, Christos ; Pinson, Pierre ; Zugno, Marco ; Morales González, Juan Miguel. / Stochastic Unit Commitment via Progressive Hedging - Extensive Analysis of Solution Methods. Proceedings of the IEEE PowerTech Conference 2015 . IEEE, 2015.
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title = "Stochastic Unit Commitment via Progressive Hedging - Extensive Analysis of Solution Methods",
abstract = "Owing to the massive deployment of renewable power production units over the last couple of decades, the use of stochastic optimization methods to solve the unit commitment problem has gained increasing attention. Solving stochastic unit commitment problems in large-scale power systems requires high computational power, as stochastic models are dramatically more complex than their deterministic counterparts. This paper provides new insight into the potential of Progressive Hedging to decrease the solution time of the stochastic unit commitment problem with a relatively small trade-off in terms of the suboptimality of the solution. Computational studies show that the run-time is at most half of what is needed to solve the original extensive formulation of the problem, when more than ten wind power scenarios are utilized. These studies demonstrate great potential for solving real-world stochastic unit commitment problems using the Progressive Hedging algorithm.",
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Ordoudis, C, Pinson, P, Zugno, M & Morales González, JM 2015, Stochastic Unit Commitment via Progressive Hedging - Extensive Analysis of Solution Methods. in Proceedings of the IEEE PowerTech Conference 2015 . IEEE, 2015 IEEE PowerTech, Eindhoven, Netherlands, 29/06/2015. https://doi.org/10.1109/PTC.2015.7232629

Stochastic Unit Commitment via Progressive Hedging - Extensive Analysis of Solution Methods. / Ordoudis, Christos; Pinson, Pierre; Zugno, Marco; Morales González, Juan Miguel.

Proceedings of the IEEE PowerTech Conference 2015 . IEEE, 2015.

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

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N2 - Owing to the massive deployment of renewable power production units over the last couple of decades, the use of stochastic optimization methods to solve the unit commitment problem has gained increasing attention. Solving stochastic unit commitment problems in large-scale power systems requires high computational power, as stochastic models are dramatically more complex than their deterministic counterparts. This paper provides new insight into the potential of Progressive Hedging to decrease the solution time of the stochastic unit commitment problem with a relatively small trade-off in terms of the suboptimality of the solution. Computational studies show that the run-time is at most half of what is needed to solve the original extensive formulation of the problem, when more than ten wind power scenarios are utilized. These studies demonstrate great potential for solving real-world stochastic unit commitment problems using the Progressive Hedging algorithm.

AB - Owing to the massive deployment of renewable power production units over the last couple of decades, the use of stochastic optimization methods to solve the unit commitment problem has gained increasing attention. Solving stochastic unit commitment problems in large-scale power systems requires high computational power, as stochastic models are dramatically more complex than their deterministic counterparts. This paper provides new insight into the potential of Progressive Hedging to decrease the solution time of the stochastic unit commitment problem with a relatively small trade-off in terms of the suboptimality of the solution. Computational studies show that the run-time is at most half of what is needed to solve the original extensive formulation of the problem, when more than ten wind power scenarios are utilized. These studies demonstrate great potential for solving real-world stochastic unit commitment problems using the Progressive Hedging algorithm.

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Ordoudis C, Pinson P, Zugno M, Morales González JM. Stochastic Unit Commitment via Progressive Hedging - Extensive Analysis of Solution Methods. In Proceedings of the IEEE PowerTech Conference 2015 . IEEE. 2015 https://doi.org/10.1109/PTC.2015.7232629