Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/6831
Title: Robust optimization-based generation self-scheduling under uncertain price
Authors: Luo, X
Chung, CY 
Yang, H
Tong, X
Keywords: Dual theory
Electricity market
Ellipsoidal uncertainties
Max-min
New model
Optimization algorithms
Power price
Programming problem
Quadratic cones
Robust optimization
Self-scheduling
Issue Date: 2011
Publisher: Hindawi Publishing Corporation
Source: Mathematical problems in engineering, v. 2011, 497014, p.1-17 How to cite?
Journal: Mathematical problems in engineering 
Abstract: This paper considers generation self-scheduling in electricity markets under uncertain price. Based on the robust optimization (denoted as RO) methodology, a new self-scheduling model, which has a complicated max-min optimization structure, is set up. By using optimal dual theory, the proposed model is reformulated to an ordinary quadratic and quadratic cone programming problems in the cases of box and ellipsoidal uncertainty, respectively. IEEE 30-bus system is used to test the new model. Some comparisons with other methods are done, and the sensitivity with respect to the uncertain set is analyzed. Comparing with the existed uncertain self-scheduling approaches, the new method has twofold characteristics. First, it does not need a prediction of distribution of random variables and just requires an estimated value and the uncertain set of power price. Second, the counterpart of RO corresponding to the self-scheduling is a simple quadratic or quadratic cone programming. This indicates that the reformulated problem can be solved by many ordinary optimization algorithms.
URI: http://hdl.handle.net/10397/6831
ISSN: 1024-123X
EISSN: 1563-5147
DOI: 10.1155/2011/497014
Rights: Copyright © 2011 Xiao Luo et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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