Simulation and detection of wind power ramps and identification of their causative atmospheric circulation patterns

Dalton, Amaris; Bekker, Bernard; Koivisto, Matti

doi:10.1016/j.epsr.2020.106936

Cited by 10 publications

(1 citation statement)

References 31 publications

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“…These results demonstrate the ability of the SOM approach for the objective classification of wind regimes. Dalton et al (2020) proposed a technique to investigate the relationship between weather systems and the resulting wind power ramp events. SOMs are utilized to classify generalized atmospheric circulation patterns from historical numerical weather prediction data.…”

Section: Related Workmentioning

confidence: 99%

A statistical Time-Of-Use tariff based wind resource clustering approach using Self-Organizing Maps

2021

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The optimized siting of grid-scale renewable generation is a viable technique to minimize the variable component of the electricity generation portfolio. This process, however, requires simulated meteorological datasets, and consequently, significant computational power to perform detailed studies. This is particularly true for countries with large geographic areas. Clustering is a viable data reduction technique that can be utilized to reduce the computational burden. This work proposes the use of Self-Organizing Maps to partition high-dimensional wind speed data using statistical features derived from Time-Of-Use tariff periods. This approach is undertaken with the view towards the optimization of wind farm siting for grid-support objectives where tariff incentivization is the main driver. The proposed approach is compared with clusters derived using Self-Organizing Maps with the temporal wind speed data for the input feature set. The results show increased cluster granularity, superior validation results and decreased execution time when compared with the temporal clustering approach.

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Section: Related Workmentioning

confidence: 99%

A statistical Time-Of-Use tariff based wind resource clustering approach using Self-Organizing Maps

2021

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Forecasting of hydrodynamic scheduling requirements for electric fields under extreme operating conditions

Li,

Mo,

Sun

et al. 2024

Electric Power Systems Research

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Case study of a bore wind-ramp event from lidar measurements and HRRR simulations over ARM Southern Great Plains

Pichugina,

Banta,

Strobach

et al. 2024

Journal of Renewable and Sustainable Energy

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The rapid change of wind speed and direction on 21 August 2017 is studied using Doppler lidar measurements at five sites of the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) facility in north-central Oklahoma. The Doppler lidar data were investigated along with meteorological variables such as temperature, humidity, and turbulence available from the large suite of instrumentation deployed at the SGP Central Facility (C1) during the Land-Atmosphere Feedback Experiment in August 2017. Lidar measurements at five sites, separated by 55–70 km, allowed us to document the development and evolution of the wind flow over the SGP area, examine synoptic conditions to understand the mechanism that leads to the ramp event, and estimate the ability of the High-Resolution Rapid Refresh model to reproduce this event. The flow feature in question is an atmospheric bore, a small-scale phenomenon that is challenging to represent in models, that was generated by a thunderstorm outflow northwest of the ARM SGP area. The small-scale nature of bores, its impact on power generation, and the modeling challenges associated with representing bores are discussed in this paper. The results also provide information about model errors between sites of different surface and vegetation types.

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Simulation and detection of wind power ramps and identification of their causative atmospheric circulation patterns

Cited by 10 publications

References 31 publications

A statistical Time-Of-Use tariff based wind resource clustering approach using Self-Organizing Maps

A statistical Time-Of-Use tariff based wind resource clustering approach using Self-Organizing Maps

Forecasting of hydrodynamic scheduling requirements for electric fields under extreme operating conditions

Case study of a bore wind-ramp event from lidar measurements and HRRR simulations over ARM Southern Great Plains

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