Modelling the effects of potential wind power integration to the Icelandic energy grid
Gunnar Pétursson, University of Iceland
ggp2@hi.is
As Iceland's power companies move towards installing their first wind turbines the problem of long term modelling of a wind integrated hydro power system is raised. Since long term planning is an essential part of an energy company's operation the need is pressing. Long term modelling of the isolated, hydro based power system in Iceland has been done in time steps of one week since that is often sufficient for the relatively slow changes is reservoir volume.
However, the operation of wind farms is a much more dynamic process than that of reservoir scheduling, and therefore the time resolution of current models is not adequate for their accurate simulation. One could simply increase the resolution but then the computation time suffers. Another option is to build a short time model within the structure of a long term model that doesn't violate the restrictions of the transmission network.
Development of an Icelandic atmospheric icing atlas and methods of short term icing forecasting
Hálfdán Ágústsson, University of Iceland
halfdana@hi.is
Atmospheric icing on overhead structures is a significant problem in many countries at both high latitudes as well as at high altitudes. Reliable forecasting and mapping of the atmospheric icing potential is therefore of importance when erecting and operating wind-power structures and electric transmission grids in regions prone to atmospheric icing.
Here, a detailed atlas of atmospheric icing in Iceland is being prepared. This work is based on several unique and accurate datasets, namely: unique records of icing events on the Icelandic transmission grid since the early 20th century, systematic observations of ice loading in test spans in the complex orography of Iceland, accurate observations of weather from a dense network of both synoptic and automatic weather stations spread throughout Iceland, as well as weather analyses dynamically downscaled to high horizontal and temporal resolution. The methods on which the icing atlas is made will serve as a base for developing operational short-term icing forecasts.
Forecasting Wind Farm Production Loss Due to Icing
Neil Davis, Technical University of Denmark
neda@dtu.dk
Icing on wind turbine blades has been recognised as a limiting factor in placing turbines in cold climates.
This is in part due to several increased risks caused by turbine icing. The first risk relates to ice which can be thrown from the blades. This ice throw presents a hazard to people or property near the turbine.
Additionally the extra mass added to the blades, often non-uniformly, adds additional loads on the turbine which can shorten its lifetime. Finally the ice can lead to reduced energy production during the icing season, limiting the performance of the wind farm. The ability to forecast icing both in the short term and climatologically could aid in the quantification of these risks, thereby improving the risk management. The scope of this PhD project involves developing the necessary tools to provide a forecast of both icing amounts, and the production loss associated with them.
The forecasting model we are developing relies on the accurate simulation of cloud amount, temperature and wind speed from a mesoscale model. These results are then coupled to a physical icing model to estimate the amount of ice which accumulates on the turbine. A combination of these outputs will finally be passed to a statistical model which will provide estimates of the production loss. The advantage of this technique is it will provide both an ice mass estimate which can potentially be used to determine loadings, and ice throw risk while at the same time providing the needed production loss estimate. We also aim to allow the tool to be useful both for wind park siting where many years of model simulation are required over a fairly large area, and for wind park production forecasting.
Due to the limited amount of data available for determining the amount of icing on the turbine blades, the production forecast is going to be the key output for validating the model. If we can gain access to addition data such as the loadings of the turbines, we will also investigate how that data might be used to estimate production loss.