Load Following Flexibility of Small Modular Reactors Coupled with Wind Farms in the Presence of Extreme Wind Conditions

2021 ◽  
Author(s):  
Joseph Rolland ◽  
Elijah Bloom ◽  
Carl Robinson ◽  
H. Bora Karayaka
Keyword(s):  
Wind Farms ◽  
Load Following ◽  
Small Modular Reactors ◽  
Extreme Wind

scholarly journals Load following with Small Modular Reactors (SMR): A real options analysis

Energy ◽  
2015 ◽  
Vol 80 ◽  
pp. 41-54 ◽  
Author(s):  
Giorgio Locatelli ◽  
Sara Boarin ◽  
Francesco Pellegrino ◽  
Marco E. Ricotti
Keyword(s):  
Real Options ◽  
Real Options Analysis ◽  
Load Following ◽  
Small Modular Reactors

scholarly journals Load following of Small Modular Reactors (SMR) by cogeneration of hydrogen: A techno-economic analysis

Energy ◽  
2018 ◽  
Vol 148 ◽  
pp. 494-505 ◽  
Author(s):  
Giorgio Locatelli ◽  
Sara Boarin ◽  
Andrea Fiordaliso ◽  
Marco E. Ricotti
Keyword(s):  
Economic Analysis ◽  
Load Following ◽  
Small Modular Reactors

scholarly journals Cogeneration: An option to facilitate load following in Small Modular Reactors

2017 ◽  
Vol 97 ◽  
pp. 153-161 ◽  
Author(s):  
Giorgio Locatelli ◽  
Andrea Fiordaliso ◽  
Sara Boarin ◽  
Marco E. Ricotti
Keyword(s):  
Load Following ◽  
Small Modular Reactors

scholarly journals Prediction of Extreme Wind Speed for Offshore Wind Farms Considering Parametrization of Surface Roughness

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 1033
Author(s):  
Xinwen Ma ◽  
Yan Chen ◽  
Wenwu Yi ◽  
Zedong Wang
Keyword(s):  
Surface Roughness ◽  
Wind Speed ◽  
Wind Field ◽  
Wind Farms ◽  
Sea Surface ◽  
Offshore Wind ◽  
Offshore Wind Farms ◽  
Sea Surface Roughness ◽  
Extreme Wind ◽  
Extreme Wind Speed

Large-scale offshore wind farms (OWF) are under construction along the southeastern coast of China, an area with a high typhoon incidence. Measured data and typhoon simulation model are used to improve the reliability of extreme wind speed (EWS) forecasts for OWF affected by typhoons in this paper. Firstly, a 70-year historical typhoon record database is statistically analyzed to fit the typhoon parameters probability distribution functions, which is used to sample key parameters when employing Monte Carlo Simulation (MCS). The sampled typhoon parameters are put into the Yan Meng (YM) wind field to generate massive virtual typhoon in the MCS. Secondly, when typhoon simulation carried out, the change in wind field roughness caused by the wind-wave coupling is studied. A simplified calculation method for realizing this phenomenon is applied by exchanging roughness length in the parametric wind field and wave model. Finally, the extreme value theory is adopted to analyze the simulated typhoon wind data, and results are verified using measured data and relevant standards codes. The EWS with 50-year recurrence of six representative OWF is predicted as application examples. The results show that the offshore EWS is generally stronger than onshore; the reason is sea surface roughness will not keep growing accordingly as the wind speed increases. The traditional prediction method does not consider this phenomenon, causing it to overestimate the sea surface roughness, and as a result, underestimate the EWS for OWF affected by typhoons. This paper’s methods make the prediction of EWS for OWF more precise, and results suggest the planer should choose stronger wind turbine in typhoon prone areas.

Selection of a leading edge noise prediction method for PNoise

2018 ◽  
pp. 214-223
Author(s):  
AM Faria ◽  
MM Pimenta ◽  
JY Saab Jr. ◽  
S Rodriguez
Keyword(s):  
Wind Turbine ◽  
Turbine Blades ◽  
Prediction Method ◽  
Leading Edge ◽  
Wind Farms ◽  
Broadband Noise ◽  
Turbulence Energy ◽  
Noise Prediction ◽  
Migration Routes ◽  
Turbulent Inflow

Wind energy expansion is worldwide followed by various limitations, i.e. land availability, the NIMBY (not in my backyard) attitude, interference on birds migration routes and so on. This undeniable expansion is pushing wind farms near populated areas throughout the years, where noise regulation is more stringent. That demands solutions for the wind turbine (WT) industry, in order to produce quieter WT units. Focusing in the subject of airfoil noise prediction, it can help the assessment and design of quieter wind turbine blades. Considering the airfoil noise as a composition of many sound sources, and in light of the fact that the main noise production mechanisms are the airfoil self-noise and the turbulent inflow (TI) noise, this work is concentrated on the latter. TI noise is classified as an interaction noise, produced by the turbulent inflow, incident on the airfoil leading edge (LE). Theoretical and semi-empirical methods for the TI noise prediction are already available, based on Amiet’s broadband noise theory. Analysis of many TI noise prediction methods is provided by this work in the literature review, as well as the turbulence energy spectrum modeling. This is then followed by comparison of the most reliable TI noise methodologies, qualitatively and quantitatively, with the error estimation, compared to the Ffowcs Williams-Hawkings solution for computational aeroacoustics. Basis for integration of airfoil inflow noise prediction into a wind turbine noise prediction code is the final goal of this work.

Disturbance of harbour porpoises during construction of the first seven offshore wind farms in Germany

2018 ◽  
Vol 596 ◽  
pp. 213-232 ◽  
Author(s):  
MJ Brandt ◽  
AC Dragon ◽  
A Diederichs ◽  
MA Bellmann ◽  
V Wahl ◽  
...  
Keyword(s):  
Wind Farms ◽  
Offshore Wind ◽  
Offshore Wind Farms

Evaluating Community Engagement and Benefit-Sharing Practices in Australian Wind Farm Development

2017 ◽  
Vol 1 (1) ◽  
pp. 1-6 ◽  
Author(s):  
Nina Lansbury Hall ◽  
Jarra Hicks ◽  
Taryn Lane ◽  
Emily Wood
Keyword(s):  
Community Engagement ◽  
Community Involvement ◽  
Wind Farm ◽  
Social Acceptance ◽  
Wind Farms ◽  
Clean Energy ◽  
Benefit Sharing ◽  
Delphi Panel ◽  
Government Organizations

The wind industry is positioned to contribute significantly to a clean energy future, yet the level of community opposition has at times led to unviable projects. Social acceptance is crucial and can be improved in part through better practice community engagement and benefit-sharing. This case study provides a “snapshot” of current community engagement and benefit-sharing practices for Australian wind farms, with a particular emphasis on practices found to be enhancing positive social outcomes in communities. Five methods were used to gather views on effective engagement and benefit-sharing: a literature review, interviews and a survey of the wind industry, a Delphi panel, and a review of community engagement plans. The overarching finding was that each community engagement and benefit-sharing initiative should be tailored to a community’s context, needs and expectations as informed by community involvement. This requires moving away from a “one size fits all” approach. This case study is relevant to wind developers, energy regulators, local communities and renewable energy-focused non-government organizations. It is applicable beyond Australia to all contexts where wind farm development has encountered conflicted societal acceptance responses.

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