This content will become publicly available on January 25, 2021
Opportunities for and challenges to further reductions in the “specific power” rating of wind turbines installed in the United States
Abstract
A wind turbine’s “specific power” rating relates its capacity to the swept area of its rotor in terms of Watt per square meter. For a given generator capacity, specific power declines as rotor size increases. In land-rich but capacity-constrained wind power markets, such as the United States, developers have an economic incentive to maximize megawatt-hours per constrained megawatt, and so have favored turbines with ever-lower specific power. To date, this trend toward lower specific power has pushed capacity factors higher while reducing the levelized cost of energy. We employ geospatial levelized cost of energy analysis across the United States to explore whether this trend is likely to continue. We find that under reasonable cost scenarios (i.e. presuming that logistical challenges from very large blades are surmountable), low-specific-power turbines could continue to be in demand going forward. Beyond levelized cost of energy, the boost in market value that low-specific-power turbines provide could become increasingly important as wind penetration grows.
- Authors:
-
[1];
[3]
- Lawrence Berkeley National Laboratory, Berkeley, CA, USA
- National Renewable Energy Laboratory, Golden, CO, USA
- National Renewable Energy Laboratory, Boulder, CO, USA
- Publication Date:
- Sponsoring Org.:
- USDOE
- OSTI Identifier:
- 1595150
- Resource Type:
- Published Article
- Journal Name:
- Wind Engineering
- Additional Journal Information:
- Journal Name: Wind Engineering; Journal ID: ISSN 0309-524X
- Publisher:
- SAGE Publications
- Country of Publication:
- United Kingdom
- Language:
- English
Citation Formats
Bolinger, Mark, Lantz, Eric, Wiser, Ryan, Hoen, Ben, Rand, Joseph, and Hammond, Robert. Opportunities for and challenges to further reductions in the “specific power” rating of wind turbines installed in the United States. United Kingdom: N. p., 2020.
Web. doi:10.1177/0309524X19901012.
Bolinger, Mark, Lantz, Eric, Wiser, Ryan, Hoen, Ben, Rand, Joseph, & Hammond, Robert. Opportunities for and challenges to further reductions in the “specific power” rating of wind turbines installed in the United States. United Kingdom. doi:10.1177/0309524X19901012.
Bolinger, Mark, Lantz, Eric, Wiser, Ryan, Hoen, Ben, Rand, Joseph, and Hammond, Robert. Sat .
"Opportunities for and challenges to further reductions in the “specific power” rating of wind turbines installed in the United States". United Kingdom. doi:10.1177/0309524X19901012.
@article{osti_1595150,
title = {Opportunities for and challenges to further reductions in the “specific power” rating of wind turbines installed in the United States},
author = {Bolinger, Mark and Lantz, Eric and Wiser, Ryan and Hoen, Ben and Rand, Joseph and Hammond, Robert},
abstractNote = {A wind turbine’s “specific power” rating relates its capacity to the swept area of its rotor in terms of Watt per square meter. For a given generator capacity, specific power declines as rotor size increases. In land-rich but capacity-constrained wind power markets, such as the United States, developers have an economic incentive to maximize megawatt-hours per constrained megawatt, and so have favored turbines with ever-lower specific power. To date, this trend toward lower specific power has pushed capacity factors higher while reducing the levelized cost of energy. We employ geospatial levelized cost of energy analysis across the United States to explore whether this trend is likely to continue. We find that under reasonable cost scenarios (i.e. presuming that logistical challenges from very large blades are surmountable), low-specific-power turbines could continue to be in demand going forward. Beyond levelized cost of energy, the boost in market value that low-specific-power turbines provide could become increasingly important as wind penetration grows.},
doi = {10.1177/0309524X19901012},
journal = {Wind Engineering},
number = ,
volume = ,
place = {United Kingdom},
year = {2020},
month = {1}
}
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