Journal
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
Volume 72, Issue -, Pages 91-98Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.ijepes.2015.02.015
Keywords
Adjustable robust optimization; Benders decomposition; Binary expansion; Correlated nodal demand uncertainty; Generation scheduling
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Funding
- CNPq Scholarship Student - Brazil
- Ministry of Science of Spain [ENE2012-30679]
- European Commission [309048]
- junta de Comunidades de Castilla-La Mancha [POII-2014-012-P]
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This paper presents a nonparametric approach based on adjustable robust optimization to consider correlated nodal demand uncertainty in a joint energy and reserve scheduling model with security constraints. In this model, up- and down-spinning reserves provided by generators are endogenously defined as a result of the optimization problem. Adjustable robust optimization is used to characterize the worst-case load variation under a given user-defined uncertainty set. This paper differs from recent previous work in two respects: (i) nonparametric correlations between nodal demands are accounted for in the uncertainty set and (ii) based on the binary expansion linearization approach, a mixed-integer linear model is provided for the optimization related to the worst-case demand. The resulting problem is formulated as a trilevel program and solved by means of Benders decomposition. Empirical results suggest that the effect of nodal correlations can be effectively captured by the robust scheduling model. (C) 2015 Elsevier Ltd. All rights reserved.
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