In this paper a new frequency domain technique is introduced for the modal identification of output-only systems, i.e. in the case where the modal parameters must be estimated without knowing the input exciting the system. By its user friendliness the technique is closely related to the classical approach where the modal parameters are estimated by simple peak picking. However, by introducing a decomposition of the spectral density function matrix, the response spectra can be separated into a set of single degree of freedom systems, each corresponding to an individual mode. By using this decomposition technique close modes can be identified with high accuracy even in the case of strong noise contamination of the signals. Also, the technique clearly indicates harmonic components in the response signals.

We consider a distribution grid interconnecting a number of consumers with flexible power consumption. Each consumer is under the jurisdiction of exactly one balancing responsible party (BRP), who buys energy at a day-ahead electricity market on behalf of the consumer. We illustrate how BRPs can utilize the flexibility of the consumers to minimize the imbalance between the consumed and the purchased energy thereby avoiding trading balancing energy at unfavorable prices. Further we show how shadow prices on the distribution lines can be used to resolve grid congestion without information sharing between the BRPs.

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The extensive growth of installed wind energy plants lead to increasing balancing problems in the power grid due to the nature of wind fields and diurnal variations in consumption. One way to overcome these problems is to move consumption to times where wind power otherwise cause overproduction and large fluctuations in prices. The paper presents a method which takes advantage of heat capacity in single-family houses using heat pumps which are anticipated to be installed in large numbers in Denmark in next decade. This type of heating gives a large time constant and it is shown possible to move consumption without compromising the comfort of house residents. In the paper an optimization exploiting forecasts of weather and energy prices combined with prediction models of house dynamics is presented. The results show that with the presented method it will be possible to move a substantial amount of energy from one time to another.