The Estimation of Wind Lull and Consumption Factor Influence on Autonomous Wind Energy System

V. Põder, T. Peets, K. Toom and A. Annuk

Department of Energy Application, Institute of Technology,
Estonian University of Life Sciences, 56 Kreutzwaldi Str., EE51014 Tartu, Estonia
e-mail: vahur.poder@emu.ee

Abstract:

Due to the stochastic output of wind generators, some kind of storage device will be necessary to ensure a constant energy supply by an autonomous energy system. The necessary storage capacity depends on wind data and consumption factor. The latter describes the ratio between average production capacity and average usage capacity. In addition to average wind speed, the frequency and duration of windless periods must be considered as well. The concept of energy lulls has been outlined to describe the influence of duration, frequency and distribution of wind less periods on a wind energy system. Location has strong influence on energy lull length; the difference in average duration between a coastal area and inland is more than two fold. Weibull distribution can be used to describe the probability of energy lulls.

Key words:

Wind energy application problems in inland Estonia

A. Annuk, E. Kokin, V. Palge, V. Põder and J. Lepa

Department of Energy Application, Institute of Technology,Estonian University of Life Sciences, Kreutzwaldi 56, 51014, Tartu;e-mail: andres.annuk@emu.ee

Abstract:

The inland regions of Estonia have not been seen as suitable economically for de-ployment of wind energy systems. Prices for technological development of wind turbines are going down, while energy prices are rising constantly. Since rural regions of Estonia are under-populated, the use of small scale wind turbine generators in these conditions is becoming more promising. Average wind speeds in mainland Estonia are 2.5–3.5 m s-1. Only a very small part of the wind speed frequency distribution (~4 ppm) exceeds 12 m s-1. More suitable for these regions are wind turbine generators which switch on at wind speeds less than 3 m s-1 and reach nominal output power at 11–12 m s-1. They have similar-looking power curves, so it is possible to model the first rising part of the curve up to maximal power by second order polynomial. Because the wind speed rarely exceeds 12 m s-1 in inland regions there is no need to model the whole power curve. The average power curve makes it possible to estimate an approximate en-ergy production of small scale wind turbine generators in a given region if the wind speed fre-quency distribution is known.

Key words:

wind energy, wind speed, wind speed frequency distri-bution, wind turbine power curve