Of the forms of renewable energy available, wind energy is at the forefront of the European (and Irish) green initiative with wind farms supplying a significant proportion of electrical energy demand. Increasingly, this type of distributed generation (DG) represents a “paradigm shift” towards increased decentralisation of energy supply. However, because of the distances of most DG from urban areas where demand is greatest, there is a loss of efficiency.

One possible solution, placing smaller wind energy systems in urban areas, faces significant challenges. However, if a renewable solution to increasing energy demand is to be achieved, energy conversion systems in cities, where populations are concentrated, must be considered. That said, assessing the feasibility of small/micro wind energy systems within the built environment is still a major challenge. These systems are aerodynamically rough and heterogeneous surfaces create complex flows that disrupt the steady-state conditions ideal for the operation of small wind turbines. In particular, a considerable amount of uncertainty is attributable to the lack of understanding concerning how turbulence within urban environments affects turbine productivity. This paper addresses some of these issues by providing an improved understanding of the complexities associated with wind energy prediction.

This research used detailed wind observations to model its turbulence characteristics. The data was obtained using a sonic anemometer that measures wind speed along three orthogonal axes to resolve the wind vector at a temporal resolution of 10Hz. That modelling emphasises the need for practical solutions by optimising standard meteorological observations of mean speeds, and associated standard deviations, to facilitate an improved appreciation of turbulence. The results of the modelling research are incorporated into a practical tool developed in EXCEL, namely the Small Wind Energy Estimation Tool (SWEET).

This tool is designed to assist engineers gain an intuitive appreciation of the limitations associated with this form of energy. It is only through an understanding of such limitations that informed decisions can be made which ultimately facilitate more intelligent installations.