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Fast chance-constrained optimization using real-time measurements with applications to power distribution systems


E. Arcari

Semester Thesis, FS16

We consider the problem faced by a Distribution Network Operator required to perform real-time assessment of the state of the network. The DNO has to guarantee the satisfaction of operational constraints within a margin of satisfaction due to stochastic disturbances, hence the problem is formulated as a chance-constrained decision problem. The challenge is to incorporate real-time measurements of the stochastic disturbances. We built an operator that decomposes samples of the stochastic disturbances updated by the measurement into a linear combination of prior samples and the measurement itself. This allowed us to separate the problem into two stages: an offline part that deals with forecast of uncertain inputs and a fast online part that includes real-time measurements. We proved that it works exactly if applied to Gaussian disturbances and preserves the compactness of the support in the case of non-Gaussian distributions. In order to do this we employ a scenario-based approach. Simulations are run on a modified version of the IEEE 123 test feeder.

Supervisors: Saverio Bolognani, Florian Dörfler


Type of Publication:

(13)Semester/Bachelor Thesis

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% Autogenerated BibTeX entry
@PhdThesis { Xxx:2017:IFA_5603
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