Is it always windy somewhere? Occurrence of low-wind-power events over large areas

Mark A. Handschy; Stephen Rose; Jay Apt

doi:10.1016/j.renene.2016.10.004

Is it always windy somewhere? Occurrence of low-wind-power events over large areas

Mark A. Handschy, Stephen Rose, Jay Apt

Science, Technology & the Environment

Research output: Contribution to journal › Article › peer-review

23 Scopus citations

Abstract

The incidence of widespread low-wind conditions is important to the reliability and economics of electric grids with large amounts of wind power. In order to investigate a future in which wind plants are geographically widespread but interconnected, we examine how frequently low generation levels occur for wind power aggregated from distant, weakly-correlated wind generators. We simulate the wind power using anemometer data from nine tall-tower sites spanning the contiguous United States. The number of low-power hours per year declines exponentially with the number of sites being aggregated. Hours with power levels below 5% of total capacity, for example, drop by a factor of about 60, from 2140 h/y for the median single site to 36 h/y for the generation aggregated from all nine sites; the standard deviation drops by a factor of 3. The systematic dependence of generation-level probability distribution “tails” on both number and power threshold is well described by the theory of Large Deviations. Combining this theory for tail behavior with the normal distribution for behavior near the mean allows us to estimate, without the use of any adjustable parameters, the entire generation duration curve as a function of the number of essentially independent sites in the array.

Original language	English (US)
Pages (from-to)	1124-1130
Number of pages	7
Journal	Renewable Energy
Volume	101
DOIs	https://doi.org/10.1016/j.renene.2016.10.004
State	Published - Feb 1 2017

Bibliographical note

Funding Information:
This material is based upon work supported by the National Science Foundation under Grant No. 1332147 and by the Doris Duke Charitable Foundation , the Richard King Mellon Foundation , the Electric Power Research Institute , and the Heinz Endowments through the RenewElec project. This research was also supported in part by the Climate and Energy Decision Making (CEDM) center , created through a cooperative agreement between the National Science Foundation (SES-0949710) and Carnegie Mellon University. The authors thank Prof. Julie Lundquist for directing them to many of the public sources of historical wind speed records used here. The authors also thank Argonne National Laboratory, Brookhaven National Laboratory, Lawrence Livermore National Laboratory, Los Alamos National Laboratory, National Renewable Energy Laboratory, the U.S. Department of Energy's Hanford Site, and its Atmospheric Research Measurement facility, NASA Kennedy Space Center, and the NOAA Earth System Research Laboratory WLEF site for providing wind speed data. They are acknowledged in more detail in the supplementary data.

Publisher Copyright:
© 2016 Elsevier Ltd

Keywords

Effective load carrying capacity (ELCC)
Geographic diversity
Large deviation theory
Wind power
Wind variability

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1016/j.renene.2016.10.004

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title = "Is it always windy somewhere? Occurrence of low-wind-power events over large areas",

abstract = "The incidence of widespread low-wind conditions is important to the reliability and economics of electric grids with large amounts of wind power. In order to investigate a future in which wind plants are geographically widespread but interconnected, we examine how frequently low generation levels occur for wind power aggregated from distant, weakly-correlated wind generators. We simulate the wind power using anemometer data from nine tall-tower sites spanning the contiguous United States. The number of low-power hours per year declines exponentially with the number of sites being aggregated. Hours with power levels below 5% of total capacity, for example, drop by a factor of about 60, from 2140 h/y for the median single site to 36 h/y for the generation aggregated from all nine sites; the standard deviation drops by a factor of 3. The systematic dependence of generation-level probability distribution “tails” on both number and power threshold is well described by the theory of Large Deviations. Combining this theory for tail behavior with the normal distribution for behavior near the mean allows us to estimate, without the use of any adjustable parameters, the entire generation duration curve as a function of the number of essentially independent sites in the array.",

keywords = "Effective load carrying capacity (ELCC), Geographic diversity, Large deviation theory, Wind power, Wind variability",

author = "Handschy, {Mark A.} and Stephen Rose and Jay Apt",

note = "Funding Information: This material is based upon work supported by the National Science Foundation under Grant No. 1332147 and by the Doris Duke Charitable Foundation , the Richard King Mellon Foundation , the Electric Power Research Institute , and the Heinz Endowments through the RenewElec project. This research was also supported in part by the Climate and Energy Decision Making (CEDM) center , created through a cooperative agreement between the National Science Foundation (SES-0949710) and Carnegie Mellon University. The authors thank Prof. Julie Lundquist for directing them to many of the public sources of historical wind speed records used here. The authors also thank Argonne National Laboratory, Brookhaven National Laboratory, Lawrence Livermore National Laboratory, Los Alamos National Laboratory, National Renewable Energy Laboratory, the U.S. Department of Energy's Hanford Site, and its Atmospheric Research Measurement facility, NASA Kennedy Space Center, and the NOAA Earth System Research Laboratory WLEF site for providing wind speed data. They are acknowledged in more detail in the supplementary data. Publisher Copyright: {\textcopyright} 2016 Elsevier Ltd",

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N2 - The incidence of widespread low-wind conditions is important to the reliability and economics of electric grids with large amounts of wind power. In order to investigate a future in which wind plants are geographically widespread but interconnected, we examine how frequently low generation levels occur for wind power aggregated from distant, weakly-correlated wind generators. We simulate the wind power using anemometer data from nine tall-tower sites spanning the contiguous United States. The number of low-power hours per year declines exponentially with the number of sites being aggregated. Hours with power levels below 5% of total capacity, for example, drop by a factor of about 60, from 2140 h/y for the median single site to 36 h/y for the generation aggregated from all nine sites; the standard deviation drops by a factor of 3. The systematic dependence of generation-level probability distribution “tails” on both number and power threshold is well described by the theory of Large Deviations. Combining this theory for tail behavior with the normal distribution for behavior near the mean allows us to estimate, without the use of any adjustable parameters, the entire generation duration curve as a function of the number of essentially independent sites in the array.

AB - The incidence of widespread low-wind conditions is important to the reliability and economics of electric grids with large amounts of wind power. In order to investigate a future in which wind plants are geographically widespread but interconnected, we examine how frequently low generation levels occur for wind power aggregated from distant, weakly-correlated wind generators. We simulate the wind power using anemometer data from nine tall-tower sites spanning the contiguous United States. The number of low-power hours per year declines exponentially with the number of sites being aggregated. Hours with power levels below 5% of total capacity, for example, drop by a factor of about 60, from 2140 h/y for the median single site to 36 h/y for the generation aggregated from all nine sites; the standard deviation drops by a factor of 3. The systematic dependence of generation-level probability distribution “tails” on both number and power threshold is well described by the theory of Large Deviations. Combining this theory for tail behavior with the normal distribution for behavior near the mean allows us to estimate, without the use of any adjustable parameters, the entire generation duration curve as a function of the number of essentially independent sites in the array.

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JO - Renewable Energy

JF - Renewable Energy

ER -

Is it always windy somewhere? Occurrence of low-wind-power events over large areas

Abstract

Bibliographical note

Keywords

UN SDGs

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