The Wind Integration National Dataset (WIND) Toolkit

Draxl, Caroline; Clifton, Andrew; Hodge, Bri‐Mathias; McCaa, Jim

doi:10.1016/j.apenergy.2015.03.121

Cited by 536 publications

(289 citation statements)

References 22 publications

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“…NWP models have also been used to generate extensive datasets of past weather events. One such dataset is the Wind Integration National Dataset (WIND) . This dataset was generated utilizing a state‐of‐the‐art NWP model to re‐forecast the years of 2007 to 2013 with a 2 km grid spacing intended for use within the wind energy community.…”

Section: Introductionmentioning

confidence: 99%

Realistic simulations of the July 1, 2011 severe wind event over the Buffalo Ridge Wind Farm

2017

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Severe winds from thunderstorm outflows pose a challenge to wind turbine arrays. They can cause significant power ramps and disruption in energy production. They can also cause extreme structural damage to turbines as was seen in the severe storm event over the Buffalo Ridge Wind Farm on July 1, 2011. At this southwestern Minnesota site, blades from multiple turbines broke away and a tower buckled in the intense winds. In this study, we attempt to characterize meteorological conditions over the Buffalo Ridge Wind Farm area during this event. The observational network included NEXRAD radars, automated surface observation stations and a wind profiler. Storm reports from the Storm Prediction Center and damage surveys provided additional insight to the in situ measurements. Even with these datasets, assessing wind speeds around turbine rotors is difficult. Thus, Weather Research and Forecasting model simulations of the event are carried out that consider current and anticipated future operational model setups. This work addresses model spatial resolution versus parameterization complexity. Parameterizations of the planetary boundary layer and microphysics processes are evaluated based on their impact on storm dynamics and the low‐level wind field. Results are also compared with the Wind Integration National Dataset, which utilizes data assimilation and an extensive continental domain. Enhanced horizontal resolution with simplistic parameterization helps increase resolved wind speeds and ramp intensity. Enhanced sophistication of microphysics parameterizations also helps increase resolved wind speeds, improve storm timing and structure and resolve higher values of turbulent kinetic energy in the lowest 1 km of the atmosphere. Copyright © 2017 John Wiley & Sons, Ltd.

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Section: Introductionmentioning

confidence: 99%

Realistic simulations of the July 1, 2011 severe wind event over the Buffalo Ridge Wind Farm

2017

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“…The selected sites are those sites that have the potential to produce the most annual energy, considering typical wind turbine power curves and buildable land area. More details on the methodology of the NREL toolkit can be found in [34].…”

Section: Generator Constraintsmentioning

confidence: 99%

“…The wind resource in each location was estimated using NREL's Wind Integration Tool Kit [34]. The kit provides estimates the wind resource at various sites across the Untied States including New York from 2007 to 2013.…”

Section: Generator Constraintsmentioning

confidence: 99%

Current and near-term GHG emissions factors from electricity production for New York State and New York City

2017

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Citation: HOWARD Abstract This paper reports estimates of average GHG emissions factors for New York State and marginal GHG emissions factors for interventions in New York City. A multi-regional unit commitment model was developed to simulate the behavior of the grid. The parameters defining the system operation were gathered from several publicly available data sources including historical hourly electricity production and fuel consumption from over one hundred power plants. Factors were estimated for a baseline year of 2011 and subsequently for the year 2025 considering planned power plant additions and retirements. Future scenarios are also developed considering different wind turbine installation growth rates and policies affecting the cost of generation from coal power plants. The work finds marginal GHG emissions factors for New York City could reduce between 30 and 36% from 540 kg CO 2 e/MWh in 2011 for all future scenarios considered. Average GHG emissions factors for New York State could reduce 9 to 39% from 215 kg CO 2 e/MWh depending on the wind growth rate and price burden on coal power plants.

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“…We build training and validation dataset using power generation data from NREL Wind 1 and Solar 2 Integration Datasets [31]. The original data has resolution of 5 minutes.…”

Section: A Data Descriptionmentioning

confidence: 99%

Model-Free Renewable Scenario Generation Using Generative Adversarial Networks

Chen

Wang

Kirschen

et al. 2018

IEEE Trans. Power Syst.

492

212

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Abstract-Scenario generation is an important step in the operation and planning of power systems with high renewable penetrations. In this work, we proposed a data-driven approach for scenario generation using generative adversarial networks, which is based on two interconnected deep neural networks. Compared with existing methods based on probabilistic models that are often hard to scale or sample from, our method is datadriven, and captures renewable energy production patterns in both temporal and spatial dimensions for a large number of correlated resources. For validation, we use wind and solar timesseries data from NREL integration data sets. We demonstrate that the proposed method is able to generate realistic wind and photovoltaic power profiles with full diversity of behaviors. We also illustrate how to generate scenarios based on different conditions of interest by using labeled data during training. For example, scenarios can be conditioned on weather events (e.g. high wind day, intense ramp events or large forecasts errors) or time of the year (e,g. solar generation for a day in July). Because of the feedforward nature of the neural networks, scenarios can be generated extremely efficiently without sophisticated sampling techniques.

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The Wind Integration National Dataset (WIND) Toolkit

Cited by 536 publications

References 22 publications

Realistic simulations of the July 1, 2011 severe wind event over the Buffalo Ridge Wind Farm

Realistic simulations of the July 1, 2011 severe wind event over the Buffalo Ridge Wind Farm

Current and near-term GHG emissions factors from electricity production for New York State and New York City

Model-Free Renewable Scenario Generation Using Generative Adversarial Networks

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