A computational framework for uncertainty integration in stochastic unit commitment with intermittent renewable energy sources

Quan, Hao; Srinivasan, Dipti; Khambadkone, A.M.; Khosravi, Abbas

doi:10.1016/j.apenergy.2015.04.103

Cited by 164 publications

(75 citation statements)

References 44 publications

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“…Others assessed integration of intermittent resources [47] such as wind [48] or solar [49], addition of demand response [50], water consumption [51] and climate impacts of the grid [52].…”

Section: Ab32mentioning

confidence: 99%

A spatially and temporally resolved model of the electricity grid – Economic vs environmental dispatch

2016

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“…Others assessed integration of intermittent resources [47] such as wind [48] or solar [49], addition of demand response [50], water consumption [51] and climate impacts of the grid [52].…”

Section: Ab32mentioning

confidence: 99%

A spatially and temporally resolved model of the electricity grid – Economic vs environmental dispatch

2016

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“…In order to incorporate forecast error uncertainties into UC and ED, scenarios generated from the conditional distributions of multiple wind farms are needed [14], [15]. Generally, when random variables are non-Gaussian and interdependent, generating scenarios, i.e., sampling, from their joint distribution is difficult [16].…”

Section: Introductionmentioning

confidence: 99%

A conditional model of wind power forecast errors and its application in scenario generation

2018

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A B S T R A C TIn power system operation, characterizing the stochastic nature of wind power is an important albeit challenging issue. It is well known that distributions of wind power forecast errors often exhibit significant variability with respect to different forecast values. Therefore, appropriate probabilistic models that can provide accurate information for conditional forecast error distributions are of great need. On the basis of Gaussian mixture model, this paper constructs analytical conditional distributions of forecast errors for multiple wind farms with respect to forecast values. The accuracy of the proposed probabilistic models is verified by using historical data. Thereafter, a fast sampling method is proposed to generate scenarios from the conditional distributions which are non-Gaussian and interdependent. The efficiency of the proposed sampling method is verified.

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“…By solving the problems, the UC schedule is obtained. Although the prior works [1][2][3][4][5][6] determine the UC schedule with considering uncertainties in different ways, they commonly need a priori knowledge of uncertainties such as their probability distributions and forecasts. In general, such a knowledge can be always provided from the past.…”

Section: Introductionmentioning

confidence: 99%

“…In [1][2][3], uncertainties are modeled as scenarios each of which represents the sequence of the realizations of uncertainties over the optimization horizon (e.g. 24 hours).…”

Section: Introductionmentioning

confidence: 99%

Contextual learning for unit commitment with renewable energy sources

Lee

Tekin

Schaar³

et al. 2016

2016 IEEE Global Conference on Signal and Information Processing (GlobalSIP)

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In this paper, we study a unit commitment (UC) problem minimizing operating costs of the power system with renewable energy sources. We develop a contextual learning algorithm for UC (CLUC) which learns which UC schedule to choose based on the context information such as past load demand and weather condition. CLUC does not require any prior knowledge on the uncertainties such as the load demand and the renewable power outputs, and learns them over time using the context information. We characterize the performance of CLUC analytically, and prove its optimality in terms of the longterm average cost. Through the simulation results, we show the performance of CLUC and the effectiveness of utilizing the context information in the UC problem.

show abstract

A computational framework for uncertainty integration in stochastic unit commitment with intermittent renewable energy sources

Cited by 164 publications

References 44 publications

A spatially and temporally resolved model of the electricity grid – Economic vs environmental dispatch

A spatially and temporally resolved model of the electricity grid – Economic vs environmental dispatch

A conditional model of wind power forecast errors and its application in scenario generation

Contextual learning for unit commitment with renewable energy sources

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