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Wind power variability, its cost, an...

Katzenstein, Warren.

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Wind power variability, its cost, and effect on power plant emissions.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Wind power variability, its cost, and effect on power plant emissions./
Author:	Katzenstein, Warren.
Description:	112 p.
Notes:	Source: Dissertation Abstracts International, Volume: 71-11, Section: B, page: 6862.
Contained By:	Dissertation Abstracts International71-11B.
Subject:	Alternative Energy. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3428742
ISBN:	9781124266398

Wind power variability, its cost, and effect on power plant emissions.
Katzenstein, Warren.

Wind power variability, its cost, and effect on power plant emissions. - 112 p.

Source: Dissertation Abstracts International, Volume: 71-11, Section: B, page: 6862.

Thesis (Ph.D.)--Carnegie Mellon University, 2010.

The recent growth in wind power is transforming the operation of electricity systems by introducing variability into utilities' generator assets. System operators are not experienced in utilizing significant sources of variable power to meet their loads and have struggled at times to keep their systems stable. As a result, system operators are learning in real-time how to incorporate wind power and its variability. This thesis is meant to help system operators have a better understanding of wind power variability and its implications for their electricity system.

ISBN: 9781124266398Subjects--Topical Terms:

1035473
Alternative Energy.

Wind power variability, its cost, and effect on power plant emissions.
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245 1 0 $a Wind power variability, its cost, and effect on power plant emissions.
300 $a 112 p.
500 $a Source: Dissertation Abstracts International, Volume: 71-11, Section: B, page: 6862.
502 $a Thesis (Ph.D.)--Carnegie Mellon University, 2010.
520 $a The recent growth in wind power is transforming the operation of electricity systems by introducing variability into utilities' generator assets. System operators are not experienced in utilizing significant sources of variable power to meet their loads and have struggled at times to keep their systems stable. As a result, system operators are learning in real-time how to incorporate wind power and its variability. This thesis is meant to help system operators have a better understanding of wind power variability and its implications for their electricity system.
520 $a Characterizing Wind Power Variability. We present the first frequency-dependent analyses of the geographic smoothing of wind power's variability, analyzing the interconnected measured output of 20 wind plants in Texas. Reductions in variability occur at frequencies corresponding to times shorter than &sim;24 hours and are quantified by measuring the departure from a Kolmogorov spectrum. At a frequency of 2.8x10-4 Hz (corresponding to 1 hour), an 87% reduction of the variability of a single wind plant is obtained by interconnecting 4 wind plants. Interconnecting the remaining 16 wind plants produces only an additional 8% reduction. We use step-change analyses and correlation coefficients to compare our results with previous studies, finding that wind power ramps up faster than it ramps down for each of the step change intervals analyzed and that correlation between the power output of wind plants 200 km away is half that of co-located wind plants. To examine variability at very low frequencies, we estimate yearly wind energy production in the Great Plains region of the United States from automated wind observations at airports covering 36 years. The estimated wind power has significant inter-annual variability and the severity of wind drought years is estimated to be about half that observed nationally for hydroelectric power.
520 $a Estimating the Cost of Wind Power Variability. We develop a metric to quantify the variability cost of individual wind plants and show its use in valuing reductions in wind power variability. Our method partitions wind energy into hourly and subhourly components and uses corresponding market prices to determine the cost of variability. The range of variability costs for 20 wind plants in ERCOT was $6 .79 to 11.5 per MWh (mean of $8 .73 +/- $1 .26 per MWh) in 2008 and $3 .16 to 5.12 per MWh (mean of $3 .90 +/- $0 .52 per MWh) in 2009. Variability costs decrease as wind plant capacity factors increase, indicating wind plants sited in locations with good wind resources cost a system less to integrate. Twenty interconnected wind plants have a variability cost of $4 .35 per MWh in 2008. The marginal benefit of interconnecting another wind plant diminishes rapidly: it is less than $3 .43 per MWh for systems with 2 wind plants already interconnected, less than $0 .7 per MWh for 4-7 wind plants, and less than $0 .2 per MWh for 8 or more wind plants. Interconnecting the 5 wind plants with the lowest variability costs produces the lowest variability cost of any 5 interconnected wind plants. Thus, wind plant variability costs are additive, with no significant nonlinearities observed.
520 $a Estimating How Wind Power Variability Affects Power Plant Emissions. Renewables portfolio standards (RPS) encourage large scale deployment of wind and solar electric power, whose power output varies rapidly even when several sites are added together. In many locations, natural gas generators are the lowest cost resource available to compensate for this variability, and must ramp up and down quickly to keep the grid stable, affecting their emissions of NOx and CO2. We model a wind or solar photovoltaic plus gas system using measured 1-minute time resolved emissions and heat rate data from two types of natural gas generators, and power data from four wind plants and one solar plant. Over a wide range of renewable penetration, we find CO2 emissions achieve &sim;80% of the emissions reductions expected if the power fluctuations caused no additional emissions. Pairing multiple turbines with a wind plant achieves &sim;77 to 95% of the emissions reductions expected. Using steam injection, gas generators achieve only 30-50% of expected NOx emissions reductions, and with dry control NO x emissions increase substantially. We quantify the interaction between state RPSs and constraints such as the NOx Clean Air Interstate Rule (CAIR), finding that states with substantial RPSs could see upward pressure on CAIR NOx permit prices, if the gas turbines we modeled are representative of the plants used to mitigate wind and solar power variability.
590 $a School code: 0041.
650 4 $a Alternative Energy. $3 1035473
650 4 $a Economics, General. $3 1017424
650 4 $a Political Science, General. $3 1017391
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710 2 $a Carnegie Mellon University. $3 1018096
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