Wind distribution and capacity factor estimation for wind turbines in the coastal region of South Africa

AbstractThe sheared-passive margin offshore Durban (South Africa) is characterized by a narrow continental shelf and steep slope hosting numerous submarine canyons. Supply of sediment to the margin is predominantly terrigenous, dominated by discharge from several short but fast-flowing rivers. International Ocean Discovery Program Expedition 361 provides a unique opportunity to investigate the role of sea-level fluctuations on the sedimentation patterns and slope instability along the South African margin. We analysed >300 sediment samples and downcore variations in P-wave, magnetic susceptibility, bioturbation intensity and bulk density from site U1474, as well as regional seismic reflection profiles to: (1) document an increase in sand input since the Mid-Pliocene; (2) associate this change to a drop in sea-level and extension of subaerial drainage systems towards the shelf-edge; (3) demonstrate that slope instability has played a key role in the evolution of the South Africa margin facing the Natal Valley. Furthermore, we highlight how the widespread occurrence of failure events reflects the tectonic control on the morphology of the shelf and slope, as well as bottom-current scour and instability of fan complexes. This information is important to improve hazard assessment in a populated coastal region with growing offshore hydrocarbon activities.

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Evaluation of AEP Predictions for Commercial Wind Farms in Sweden

Applied Sciences ◽

10.3390/app10227995 ◽

2020 ◽

Vol 10 (22) ◽

pp. 7995

Author(s):

Erik Möllerström ◽

Daniel Lindholm

Keyword(s):

South Africa ◽

Wind Turbine ◽

Wind Turbines ◽

Energy Production ◽

Wind Farms ◽

Project Planning ◽

Data Availability ◽

Energy Calculation ◽

The Mean ◽

Over Time

Based on data from 1162 wind turbines, with a rated power of at least 1.8 MW, installed in Sweden after 2005, the accuracy of the annual energy production (AEP) predictions from the project planning phases has been compared to the wind-index-corrected production. Both the production and the predicted AEP data come from the database Vindstat, which collects information directly from wind turbine owners. The mean error was 7.1%, which means that, overall, the predicted AEP has been overestimated. The overestimation was higher for wind turbines situated in open terrain than in forest areas and was higher overall than that previously established for the British Isles and South Africa. Dividing the result over the installation year, the improvement which had been expected due to the continuous refinement of the methods and better data availability, was not observed over time. The major uncertainty comes from the predicted AEP as reported by wind turbine owners to the Vindstat database, which, for some cases, might not come from the wind energy calculation from the planning phase (i.e., the P50-value).

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Wind energy resource assessment of Kütahya, Turkey using WAsP and layout optimization

Proceedings of the Institution of Mechanical Engineers Part A Journal of Power and Energy ◽

10.1177/0957650920936030 ◽

2020 ◽

pp. 095765092093603

Author(s):

Onur Koşar ◽

Mustafa Arif Özgür

Keyword(s):

Wind Speed ◽

Wind Turbines ◽

Wind Farm ◽

Real Life ◽

Energy Resource ◽

Resource Assessment ◽

Capacity Factor ◽

Strong Wind ◽

Wind Potential ◽

Shear Turbulence

Kütahya is considered as a candidate region for a wind farm investment due to Turkey's 2023 energy targets and its proximity to other wind farm investments. In this study, two years of wind data collected from a hill near the Evliya Çelebi Campus of Kütahya Dumlupınar University was used to evaluate the wind farm potential of Kütahya. First, the wind speed, wind direction, wind shear, turbulence intensity and wind speed ramp characteristics were determined. Second, the WAsP software was used to create a wind atlas for the region. Three sites with strong wind potential were evaluated. A techno-economic analysis was conducted using five types of wind turbines selected from the WAsP database. Third, optimization of a wind farm layout was conducted by considering different hub height options for 14 commercial wind turbines using MATLAB software. It was shown theoretically that a wind farm with a power capacity of 25 MW can operate with a capacity factor of 35%. However, due to the relatively high topographical ruggedness index on the wind farm site, the calculated value for the capacity factor could not be reached in a real-life application.

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Wind Turbines Capacity Factor Modeling—A Novel Approach

IEEE Transactions on Power Systems ◽

10.1109/tpwrs.2009.2023274 ◽

2009 ◽

Vol 24 (3) ◽

pp. 1637-1638 ◽

Cited By ~ 39

Author(s):

M.H. Albadi ◽

E.F. El-Saadany

Keyword(s):

Wind Turbines ◽

Capacity Factor ◽

Novel Approach ◽

Factor Modeling

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97/02994 Capacity factor of wind turbines

Fuel and Energy Abstracts ◽

10.1016/s0140-6701(97)84867-9 ◽

1997 ◽

Vol 38 (4) ◽

pp. 246

Keyword(s):

Wind Turbines ◽

Capacity Factor

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Optimization of Wind Turbine Placement in Offshore Wind Farms

Volume 2: 31st Computers and Information in Engineering Conference, Parts A and B ◽

10.1115/detc2011-47935 ◽

2011 ◽

Author(s):

Simeng Li ◽

J. Iwan D. Alexander

Keyword(s):

Wind Turbines ◽

Spatial Configuration ◽

Unit Cost ◽

Wind Farms ◽

Offshore Wind ◽

Offshore Wind Farms ◽

Wind Speeds ◽

Wake Interactions ◽

Wake Model ◽

Wind Distribution

In this paper, a Genetic Algorithm is used to find optimized spatial configurations of wind turbines in offshore or flat terrain wind farms. The optimization is made by obtaining maximizing power output per unit cost. A wake model which permits the calculation of single wakes, multiple wakes and wake interactions is employed to estimate wind speeds at each turbine for a given external wind distribution function and a given spatial configuration. The optimization is applied to cases of unidirectional wind, variable direction winds and variable wind speed. The placement of a turbine can be set at any location following the approach of Mittal et al. Results are obtained for different spacing limits between turbines and wind farms of different sizes. The results for some patterns of optimized placements of wind turbines are discussed in the context of the wind distributions and the wake model employed.

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Wind-farm layout optimisation using a hybrid Jensen–LES approach

10.5194/wes-2016-15 ◽

2016 ◽

Cited By ~ 1

Author(s):

Vahid S. Bokharaie ◽

Pieter Bauweraerts ◽

Johan Meyers

Keyword(s):

Wind Turbines ◽

Energy Production ◽

Wind Farm ◽

Rectangular Domain ◽

Hybrid Combination ◽

Wake Interaction ◽

Large Eddy ◽

Wind Distribution ◽

Online Tuning

Abstract. Given a wind-farm with known dimensions and number of wind-turbines, we try to find the optimum positioning of wind-turbines that maximises wind-farm energy production. In practise, given that optimisation has to be performed for many wind directions and taking into account the yearly wind distribution, such an optimisation is computationally only feasible using fast engineering wake models such as, e.g., the Jensen model. These models are known to have accuracy issues, in particular since their representation of wake interaction is very simple. In the present work, we propose an optimisation approach that is based on a hybrid combination of Large-Eddy Simulations (LES) and the Jensen model, in which optimisation is mainly performed using the Jensen model, and LES is used at a few points only during optimisation for online tuning of the wake-expansion coefficient in the Jensen model, and for validation of the results. An optimisation case study is considered, in which the placement of 30 turbines in a 4 by 3 km rectangular domain is optimised in a neutral atmospheric boundary layer. Both optimisation for single wind direction, and multiple wind directions are discussed.

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Wind-farm layout optimisation using a hybrid Jensen–LES approach

Wind Energy Science ◽

10.5194/wes-1-311-2016 ◽

2016 ◽

Vol 1 (2) ◽

pp. 311-325 ◽

Cited By ~ 7

Author(s):

Vahid S. Bokharaie ◽

Pieter Bauweraerts ◽

Johan Meyers

Keyword(s):

Wind Turbines ◽

Energy Production ◽

Wind Farm ◽

Rectangular Domain ◽

Eddy Simulation ◽

Wake Interaction ◽

Large Eddy ◽

Wind Distribution ◽

Online Tuning

Abstract. Given a wind farm with known dimensions and number of wind turbines, we try to find the optimum positioning of wind turbines that maximises wind-farm energy production. In practice, given that optimisation has to be performed for many wind directions, and taking into account the yearly wind distribution, such an optimisation is computationally only feasible using fast engineering wake models such as the Jensen model. These models are known to have accuracy issues, in particular since their representation of wake interaction is very simple. In the present work, we propose an optimisation approach that is based on a hybrid combination of large-eddy simulation (LES) and the Jensen model; in this approach, optimisation is mainly performed using the Jensen model, and LES is used at a few points only during optimisation for online tuning of the wake-expansion coefficient in the Jensen model, as well as for validation of the results. An optimisation case study is considered, in which the placement of 30 turbines in a 4 km by 3 km rectangular domain is optimised in a neutral atmospheric boundary layer. Optimisation for both a single wind direction and multiple wind directions is discussed.

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