Carsten Hoyer-Klick

Stability and Power Quality Issues in Microgrids Under Weather Disturbances

In this paper, a stability study of Kythnos island power system was investigated under power generation deviation due to solar insolation variations measured from ground and Earth observation. The system was tested under disturbances, like sudden shadowing of the photovoltaic generators due to cloudiness. It was assumed that the cloud was completely covering the island. Two other disturbances for a 24-h simulation were taken from real meteorological data, one from ground measurements and the other based on satellite data. Simulation tests for the first case show that the system is generally stable for penetration lower than 50%, while it becomes unstable at higher levels. Additionally, the shadowing effect was slow enough in order to cope with it. For the second case, the system is operating very close to the limits for high PV penetration. To optimize operation a satellite based nowcasting and short-term forecasting could be used. A 15-min advance notice that cloud cover is eminent would be adequate to start up the back up systems and avoid instability or black out events. Such a strategy allows reserve need reductions in clear sky cases, as changes in weather conditions can be predicted early enough in advance.

WISENT: e-Science for Energy Meteorology

Our energy production increasingly depends on renewable energy sources, which impose new challenges for distributed and decentralized systems. One problem is that the availability of renewable energy sources such as wind and solar is not continuous as it is affected by meteorological factors. The challenge is to develop forecast methods capable of determining the level of power generation in near real-time in order to control power plants for optimal energy production. Another scenario is the identification of optimal locations for such power plants. In our collaborative project, these tasks are investigated in the domain of energy meteorology. For that purpose large data sources from many different sensors (e.g., satellites and ground stations) are the base for complex computations. The idea is to parallelize these computations in order to obtain significant speedup. This paper reports on an ongoing project employing Grid technologies in that context. Our approach to processing large data sets from a variety of heterogeneous data sources as well as ideas for parallel and distributed computing in energy meteorology are presented. Preliminary experience with several Grid middleware systems in our application scenario is discussed.