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# Accommodating high-dimensional uncertainty models in multi-stage power system optimization

Student(en):

Betreuer:

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, increasingly sophisticated numerical weather prediction models are able to run multiple scenarios of wind speed and solar radiation. Such groups of scenarios are referred to as an ensemble forecast.
These 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. We therefore need to be able to do the following:
1. Develop approaches for reducing the dimension of the high-dimensional data set, but still capturing the main correlations (in space and time) of the forecast, in order to avoid plugging supercomputer outputs directly into an optimization routine;
2. Develop general methods for reducing the representation of the uncertainty set surrounding a "nominal" forecast, to avoid having to include the full representation in an optimization. This should ideally provide some (probabilistic) guarantees on the safety of the system, given that the weather model only includes a limited number of scenarios;
3. Develop novel optimization formulations exploiting as much of the above two points as possible, and test the approach on a suitable power system driven by the weather model.

This project will build on early-stage existing work on the subject, and in collaboration with a colleague from the UK with access to the leading-edge model outputs linked above. For background on the multi-stage optimization approach previously developed, see this paper.

First three components of a wind forecast uncertainty model (as measured from a covariance matrix derived from the output of the MOGREPS model), for 336 wind farms across the UK.

Weitere Informationen
Professor:

John Lygeros
Projektcharakteristik:

Typ:
Art der Arbeit: 70% theory, 30% implementation
Voraussetzungen: Optimization knowledge, and some comfort with (and interest in) playing with large data files
Anzahl StudentInnen: 1
Status: open
Projektstart: Feb/March 2017
Semester: FS 2017

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