Wind power forecasting uncertainty and unit commitment
被引:268
作者:
Wang, J.
论文数: 0引用数: 0
h-index: 0
机构:
Argonne Natl Lab, Argonne, IL 60439 USAArgonne Natl Lab, Argonne, IL 60439 USA
Wang, J.
[1
]
Botterud, A.
论文数: 0引用数: 0
h-index: 0
机构:
Argonne Natl Lab, Argonne, IL 60439 USAArgonne Natl Lab, Argonne, IL 60439 USA
Botterud, A.
[1
]
Bessa, R.
论文数: 0引用数: 0
h-index: 0
机构:
Univ Porto, Fac Engn, INESC Porto, P-4200465 Oporto, Portugal
Univ Porto, Fac Engn, FEUP, P-4200465 Oporto, PortugalArgonne Natl Lab, Argonne, IL 60439 USA
Bessa, R.
[2
,3
]
Keko, H.
论文数: 0引用数: 0
h-index: 0
机构:
Univ Porto, Fac Engn, INESC Porto, P-4200465 Oporto, Portugal
Univ Porto, Fac Engn, FEUP, P-4200465 Oporto, PortugalArgonne Natl Lab, Argonne, IL 60439 USA
Keko, H.
[2
,3
]
Carvalho, L.
论文数: 0引用数: 0
h-index: 0
机构:
Univ Porto, Fac Engn, INESC Porto, P-4200465 Oporto, Portugal
Univ Porto, Fac Engn, FEUP, P-4200465 Oporto, PortugalArgonne Natl Lab, Argonne, IL 60439 USA
Carvalho, L.
[2
,3
]
Issicaba, D.
论文数: 0引用数: 0
h-index: 0
机构:
Univ Porto, Fac Engn, INESC Porto, P-4200465 Oporto, Portugal
Univ Porto, Fac Engn, FEUP, P-4200465 Oporto, PortugalArgonne Natl Lab, Argonne, IL 60439 USA
Issicaba, D.
[2
,3
]
Sumaili, J.
论文数: 0引用数: 0
h-index: 0
机构:
Univ Porto, Fac Engn, INESC Porto, P-4200465 Oporto, Portugal
Univ Porto, Fac Engn, FEUP, P-4200465 Oporto, PortugalArgonne Natl Lab, Argonne, IL 60439 USA
Sumaili, J.
[2
,3
]
Miranda, V.
论文数: 0引用数: 0
h-index: 0
机构:
Univ Porto, Fac Engn, INESC Porto, P-4200465 Oporto, Portugal
Univ Porto, Fac Engn, FEUP, P-4200465 Oporto, PortugalArgonne Natl Lab, Argonne, IL 60439 USA
Miranda, V.
[2
,3
]
机构:
[1] Argonne Natl Lab, Argonne, IL 60439 USA
[2] Univ Porto, Fac Engn, INESC Porto, P-4200465 Oporto, Portugal
[3] Univ Porto, Fac Engn, FEUP, P-4200465 Oporto, Portugal
In this paper, we investigate the representation of wind power forecasting (WPF) uncertainty in the unit commitment (UC) problem. While deterministic approaches use a point forecast of wind power output, WPF uncertainty in the stochastic UC alternative is captured by a number of scenarios that include cross-temporal dependency. A comparison among a diversity of UC strategies (based on a set of realistic experiments) is presented. The results indicate that representing WPF uncertainty with wind power scenarios that rely on stochastic UC has advantages over deterministic approaches that mimic the classical models. Moreover, the stochastic model provides a rational and adaptive way to provide adequate spinning reserves at every hour, as opposed to increasing reserves to predefined, fixed margins that cannot account either for the system's costs or its assumed risks. (C) 2011 Elsevier Ltd. All rights reserved.