Multilinear Singular Value Decomposition of Space- and Time-Coupled Weather Forecasts |
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Betreuer: Annika Eichler, Joe Warrington |
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Beschreibung: Efficient real-time operation of power systems is made increasingly difficult by the growth of intermittent renewable energy sources (primarily wind and solar power). In order to forecast these power, numerical weather prediction models are able to run multiple scenarios of wind speed and solar radiation. These weather forecasts are coupling time and space in different expected scenarios. The forecasts represent a huge amount of data that can (in principle) be used to drive a real-time power system optimization algorithm. In particular, variations between the scenarios in the ensemble forecast can be used to assess the uncertainty in the prediction of wind and solar power output. Sizing such uncertainty is critical to procuring an appropriate amount of reserves and deciding how to operate conventional generators and storage devices around the intermittent power infeeds. However, use of the full forecast data set will lead to an intractable optimization problem. Therefore, a low-dimensional representation of the forecast data is needed. The multidimensional forecast information can be naturally represented as a three-dimensional tensor with the dimensions being space, time and the scenarios. For a low-dimensional representation a multilinear singular value decomposition is to be applied to incorporate the coupling in time and space, which extends the concept of singular value decomposition of matrices as it is applied in previous work, see this paper. Mode analysis of wind power forecast errors for 336 wind farms located across Britain, derived from ensemble forecast outputs. Weitere Informationen |
Professor: John Lygeros |
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Projektcharakteristik: Typ: Art der Arbeit: 70% theory, 30% implementation Voraussetzungen: Some optimization knowledge is beneficial, and interest in working with large data sets would be a plus | |
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Anzahl StudentInnen: 1 Status: open | |
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Projektstart: Spring 2018 Semester: Autumn 2017 | |
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