2011 IEEE Power and Energy Society General Meeting 2011
DOI: 10.1109/pes.2011.6039388
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Wind power forecasting error distributions over multiple timescales

Abstract: Wind forecasting is an important consideration in integrating large amounts of wind power into the electricity grid. The wind power forecast error distribution assumed can have a large impact on the confidence intervals produced in wind power forecasting. In this work we examine the shape of the persistence model error distribution for ten different wind plants in the ERCOT system over multiple timescales. Comparisons are made between the experimental distribution shape and that of the normal distribution. The… Show more

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Cited by 250 publications
(204 citation statements)
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“…Hence, the concept is general enough that can be extended to any scenario generation & reduction method. The first phase consists of characterizing the random parameter (see Section II), for more sophisticated formulations regarding wind forecast error consult [27,28] and the references therein. The second phase of the proposal is to analyze the quality of survival scenario set (after applying reduction techniques) but not in terms of representing the true distribution of the random parameter.…”
Section: A Stability Requirementmentioning
confidence: 99%
“…Hence, the concept is general enough that can be extended to any scenario generation & reduction method. The first phase consists of characterizing the random parameter (see Section II), for more sophisticated formulations regarding wind forecast error consult [27,28] and the references therein. The second phase of the proposal is to analyze the quality of survival scenario set (after applying reduction techniques) but not in terms of representing the true distribution of the random parameter.…”
Section: A Stability Requirementmentioning
confidence: 99%
“…The parameters of the probability distributions are gathered in Table I. The parameters of the beta distributions have been taken following the analysis made in [25], where the 15-minute forecast error for a single wind plant is fitted to a similar beta distribution. The base power for the per-unit system is 100 MW.…”
Section: A System Setupmentioning
confidence: 99%
“…Previous research has shown, however, that wind power forecast errors are not Gaussian distributed. Instead, beta and hyperbolic distributions seem appropriate [7], [8]. In [9], the same CCOPF formulation as in [6] is used, but the solution method is extended in order to account for non-Gaussian distributions.…”
Section: Introductionmentioning
confidence: 99%
“…We chose values increasing from ±5% to ±20% of the installed capacity for the absolute bounds and a rate constraint of 20% according to Hodge and Milligan [2011] and Wan [2011].…”
Section: Network Model and Devicesmentioning
confidence: 99%