Optimization of wind turbine micro-siting for reducing the sensitivity of power generation to wind direction

Abstract

a b s t r a c tIn the optimization of wind turbine micro-siting of wind farms, the major target is to maximize the total energy yield. But considering from the aspect of the power grid, the sensitivity of wind power generation to varying incoming wind direction is also an essential factor. However, most existing optimization approaches on wind turbine micro-siting are focused on increasing the total power yield only. In this paper, by employing computational fluid dynamics and the virtual particle model for the simulation of turbine wake flow, a sensitivity index is proposed to quantitatively evaluate the variation of power generation under varying wind direction. Typical turbine layouts obtained by existing power optimization approaches are evaluated for stability. Results indicate that regularly arranged turbine layouts are not suitable for stable power production. Based on solutions from the power optimization, a secondstage optimization using Particle Swarm Optimization algorithm is presented. The proposed optimization method adjusts the positions of the turbines locally, aiming at increasing the stability of wind farm power generation without damaging its advantage of high power yield. Case studies on flat terrain and complex terrain both demonstrate the effectiveness of the present local adjustment optimization method.

Offshore wind climate analysis and variability in the Mediterranean Sea

“…From the stochastic point of view, wind direction is also a random variable, characterized by its inherent variability and its association structure with wind speed. Moreover, the modelling of wind direction seems to play an increasingly important role in a vast variety of applications such as the fast developing technology of floating wind turbines (Soukissian, 2014) and the micro-siting of offshore wind farms (Song et al, 2016), the design/static stability of bridge structures (Wang et al, 2013), beach morphological dynamics and sediment transport (Sedrati and Anthony, 2007;Walker et al, 2009), etc. For wind climate analysis and identification of wind variability, long-term wind data are required. For local assessment purposes, usually, wind measurements at offshore locations are performed by installing a meteorological mast or a lidar on an existing offshore structure or an on-site buoy.…”

Satellite-Based Offshore Wind Resource Assessment in the Mediterranean Sea

“…2 Regarding wind speed, apart from annual and seasonal mean values, the spatial distributions of mean annual and interannual variability are also provided. Regarding wind direction, although it is often neglected in the offshore wind energy assessment [13], [14], it is a critical parameter used in the micrositing of wind turbines within an OWF to minimize wake effects in varying wind directions, as is emphasized in [15] and [16]. In particular, in [15], the effects on the efficiency of turbine inability for optimal aligning to the wind direction (due to meandering wind caused by wakes) were examined.…”

Quantifying the Influences on Probabilistic Wind Power Forecasts

“…It is also possible to develop a framework to examine and evaluate features that allow the assessment of potential locations for new wind or solar parks as shown in [6]. The authors of [8] use sensitivity analysis and swarm optimization to obtain the best placement of wind turbines in wind parks [8]. In [9], sensitivity analysis is used to show that weather data has a higher impact than technical features in wind power time series modeling.…”