2015 Cost of Wind Energy Review

Mone, Christopher; Hand, M.; Bolinger, Mark; Rand, Joseph; Heimiller, D.; Ho, Jonathan

doi:10.2172/1366436

Cited by 43 publications

(52 citation statements)

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“…Discuss., https://doi.where capital expenditures (CapEx) and operational expenditures (OpEx) make up the cost of building and running a wind turbine. Our goal is to minimize the cost of wind energy, enabling the sale of more wind turbines in an effort to meet our climate goals.Operational expenditures are non-negligible, but make up roughly 15 % of the total cost, according to a study of the average 2015 offshore wind turbine (Mone et al, 2015). Capital expenditures include the wind turbine parts and balance-of-station 5 costs.…”

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confidence: 99%

System-level design studies for large rotors

Zalkind¹,

Ananda²,

Chetan³

et al. 2019

Preprint

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Abstract. We examine the effect of rotor design choices on the power capture and structural loading of each major wind turbine component. A steady-state, harmonic model derived from simulations using the NREL aeroelastic code FAST is developed to reduce computational expense while evaluating design trade-offs for rotors with radii greater than 100 m. Design studies are performed, which focus on blade aerodynamic and structural parameters as well as different hub configurations and nacelle placements atop the tower. The effects of tower design and closed-loop control are also analyzed. Design loads are calculated according to the IEC design standards and used to calibrate the harmonic model and quantify uncertainty. Our design studies highlight both industry trends and innovative designs: we progress from a conventional, upwind, 3-bladed rotor, to a rotor with longer, more slender blades that is downwind and 2-bladed. For a 13 MW design, we show that increasing the blade length by 25 m while decreasing the induction factor of the rotor increases annual energy capture by 11 % while constraining peak blade loads. A downwind, 2-bladed rotor design is analyzed, with a focus on its ability to reduce peak blade loads by 10 % per 5 deg. of cone angle, and also reduce total blade mass. However, when compared to conventional, 3-bladed, upwind designs, the peak main bearing load of the up-scaled, downwind, 2-bladed rotor is increased by 280 %. Optimized teeter configurations and individual pitch control can reduce non-rotating damage equivalent loads by 45 % and 22 %, respectively, compared with fixed-hub designs.

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confidence: 99%

System-level design studies for large rotors

Zalkind¹,

Ananda²,

Chetan³

et al. 2019

Preprint

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“…To understand the influence of the wind resource on the capacity factor, we may then consider the prevailing wind conditions for one specific location, Los Vientos, Texas. This is a TRG 5 location of wind speed range 6.9 to 11.1, average speed 7.5 m/s, and expected capacity factor 40.7%. Los Vientos, Texas, is the location of a 912 MW wind farm complex, including different units, I(1A), II(1B), III, IV, and V, of rated power 200, 202, 200, 200, and 110 MW, that were completed recently, between 2012 and 2016.…”

Section: Methodsmentioning

confidence: 98%

“…The best performing wind farm has an average power of 50% the rated power, and worst‐performing wind farm has an average power of 15% the rated power. The wind speed range for the TRG considered, mostly 5 to 7, is 6.9 to 11.1 to 5.4 to 8.3 m/s, the average speed is 7.5 to 6.2 m/s, and the expected capacity factor is 40.7% to 30.8%. The actual energy production differs between wind farms, even if sharing about the same prevailing wind speed conditions and even with similar orography and turbines' relative location.…”

Section: Methodsmentioning

confidence: 99%

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Energy storage requirements to address wind energy variability

Boretti

2019

Energy Storage

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Aim of this contribution is to show the variability of wind farm capacity factors (the actual generation power vs the nominal power) to highlight the needs of energy storage. Low‐frequency (monthly and annual values) cases are shown for wind farms in the continental United States, and high‐frequency (5 minutes values) cases are shown for south east Australia (all of Australia excluding Western Australia and Northern Territory). In the United States, wind farms produce electricity on average over a year at a rate of around 30% to 35% of the capacity but with strong variability, seasonal and interannual, between different techno‐resource group (TRG) areas for wind resource and within the same wind energy resource TRG area. In south east Australia, the annual average capacity factors from wind are around 30% to 35%. There are many days every month where the average capacity factors may exceed 60% but also drop below 10%, sometimes about 5%. The proposed results make even stronger the case for energy storage facilities, if integration with conventional energy resources is not welcomed.

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“…There are several articles from US National Renewable Energy Lab that discuss the cost of turbine components . Here, we use Mone et al, 36 which provides cost values as a function of turbine size and whether it is onshore or offshore.…”

Section: Optimizing Wind Farm Lidar Usementioning

confidence: 99%

Maximizing the returns of LIDAR systems in wind farms for yaw error correction applications

Bakhshi

Sandborn

2020

Wind Energy

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Wind energy is an important source of renewable energy with significant untapped potential around the world. However, the cost of wind energy production is high, and efforts to lower the cost of energy generation will help enable more widespread use of wind energy. Yaw error reduces the efficiency of turbines as well as lowers the reliability of key components in turbines. Light detection and ranging (LIDAR) devices can correct the yaw error; however, they are expensive, and there is a tradeoff between their costs and benefits. In this study, a stochastic discrete-event simula-

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2015 Cost of Wind Energy Review

Cited by 43 publications

References 0 publications

System-level design studies for large rotors

System-level design studies for large rotors

Energy storage requirements to address wind energy variability

Maximizing the returns of LIDAR systems in wind farms for yaw error correction applications

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