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Energy Systems

, Volume 10, Issue 3, pp 517–541 | Cite as

Robust optimization vs. stochastic programming incorporating risk measures for unit commitment with uncertain variable renewable generation

  • Narges KazemzadehEmail author
  • Sarah M. Ryan
  • Mahdi Hamzeei
Original Paper

Abstract

Unit commitment seeks the most cost effective generator commitment schedule for an electric power system to meet net load, defined as the difference between the load and the output of renewable generation, while satisfying the operational constraints on transmission system and generation resources. Stochastic programming and robust optimization are the most widely studied approaches for unit commitment under net load uncertainty. We incorporate risk considerations in these approaches and investigate their comparative performance for a multi-bus power system in terms of economic efficiency as well as the risk associated with the commitment decisions. We explicitly account for risk, via Conditional Value at Risk (CVaR) in the stochastic programming objective function, and by employing a CVaR-based uncertainty set in the robust optimization formulation. The numerical results indicate that the stochastic program with CVaR evaluated in a low-probability tail is able to achieve better cost-risk trade-offs than the robust formulation with less conservative preferences. The CVaR-based uncertainty set with the most conservative parameter settings outperforms an uncertainty set based only on ranges.

Keywords

Unit commitment Renewable energy Stochastic programming Robust optimization CVaR 

Notes

Acknowledgements

Funding was provided by Iowa Energy Center (Grant no. OG-14-014).

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2017

Authors and Affiliations

  • Narges Kazemzadeh
    • 1
    Email author
  • Sarah M. Ryan
    • 1
  • Mahdi Hamzeei
    • 2
  1. 1.Department of Industrial and Manufacturing Systems EngineeringIowa State UniversityAmesUSA
  2. 2.University of MarylandBaltimoreUSA

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