Campus Units

Industrial and Manufacturing Systems Engineering

Document Type

Article

Publication Version

Accepted Manuscript

Publication Date

12-15-2017

Journal or Book Title

Energy Systems

DOI

10.1007/s12667-017-0265-5

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.

Comments

This is an accepted manuscript published as Kazemzadeh, Narges, Sarah M. Ryan, and Mahdi Hamzeei. "Robust optimization vs. stochastic programming incorporating risk measures for unit commitment with uncertain variable renewable generation." Energy Systems: 1-25.

Copyright Owner

Springer Verlag

Language

en

File Format

application/pdf

Available for download on Saturday, December 15, 2018

Published Version

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