Unscented transformation-based fast scheduling optimization for large-scale unit commitment considering uncertainties of wind and solar power

Zheng, Jiehui; Kou, Y. N.; Li, Zhigang

doi:10.1109/pesgm.2017.8274257

Cited by 3 publications

(2 citation statements)

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“…Now with static information, z true¯ and C zz selects (2 n + 1) samples from input variable to formulate z. The uncertain output y related to z by a non-linear function of unit commitment (f), unscented transformation (UT) can compute the mean and covariance of the output variable, y true¯ and C yy by the steps given in (Zheng et al, 2017). Selects ( 2n + 1 ) samples from input variables using following equations. here s k is k th sample of input variables, α is scaling parameter, w 0 is the weight of central moments sample and chol = C zz is the square root matrix with k th row and it is represented by chol (k, :).…”

Section: Uncertainty Of Solar and Windmentioning

confidence: 99%

Section: Uncertainty Of Solar and Windmentioning

confidence: 99%

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Investigation of solution techniques of unit commitment problems: A review

2021

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The Unit Commitment (UC) is a significant act of optimization in day-to-day operational planning of modern power systems. After load forecasting, UC is the subsequent step in the planning process. The electric utilities decide in advance which units are to start-up, when to connect them to the network, the sequence in which the generating units should be shut down and for how long. In view of the above, this paper attempts on presenting a thorough and precise review of the recent approaches applied in optimizing UC problems, incorporating both stochastic and deterministic loads, based on various peer reviewed published research papers of reputed journals. It emphasizes on non-conventional energy and distributed power generating systems along with deregulated and regulated environment. Along with an overview, a comprehensive analysis of the UC algorithms reported in the recent past since 2015 has been discussed for the assistance of new researchers concerned with this domain.

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