Author(s):  Bertényi, Tamás;

The SmartCoDe project is looking at the smart integration of local energy neighbourhoods and the grid, including local energy production (LEP) by small-scale distributed energy generation technologies such as photo-voltaic or wind energy. Including LEPs into the smart-grid integration equation makes the overall scenario more attractive. The ability to predict site-specfic energy yield from the LEPs can translate into increased value for the building owner. Ultimately, it is hoped that energy forecasting can provide a route for greater degree of adoption of otherwise volatile renewable energy sources.

For purposes of demonstration, SmartCoDe is considering the specific example of a small-scale wind turbine. While the capability of predicting wind-resource dependant energy yield is becoming more common for large wind farms, the challenges are significantly increased for small-scale turbines located low in the boundary layer in areas of increased terrain roughness. The problem is compounded because these installations can rarely afford the time or resource to conduct a proper wind resource assessment.

Broadly speaking, the forecasting can be categorised by the time period: long-term energy forecasting is about predicting the energy performance over the course of years or even the lifetime of the product. This involves understanding of the turbine performance as well as the local wind. The approach taken is to combine available macro-scale wind resource maps with short-period measurements of the proposed installation site. Key measured characteristics of the wind resource can be used as a basis to correct for local variation. A properly constructed energy model can then be used to predict the resulting energy yield.

Meanwhile, short-term forecasting, on the order of tens of minutes or hours, can play a valuable role in smart grid integration strategies. The forecast of expected energy yields over a range of periods (bounded by measures of confidence) can form part of a local energy energy management approach together with demand side management of local energy using products and, optionally, local energy storage. Short-term forecasting builds upon the long-term forecasting by including statistical methods built on a database of local historical performance.

This talk presents on-going research in the area of energy forecasting for small-scale turbines, including correcting macro-scale wind resource for local micro-scale effects, energy yield modelling of systems, and statistical methods for predicting short-term energy yield.

Publication Date:  2010/11/16
Location of Publication:  SmartCoDe ECWS, Vienna
Keyword(s):  Renewable Energies; Energy Efficiency; Energy Management; Research/Education;
Download(s):