Robust approximation of chance constrained DC optimal power flow under decision-dependent uncertainty

  • We propose a mathematical optimization model and its solution for joint chance constrained DC Optimal Power Flow. In this application, it is particularly important that there is a high probability of transmission limits being satisfied, even in the case of uncertain or fluctuating feed-in from renewable energy sources. In critical network situations where the network risks overload, renewable energy feed-in has to be curtailed by the transmission system operator (TSO). The TSO can reduce the feed-in in discrete steps at each network node. The proposed optimization model minimizes curtailment while ensuring that there is a high probability of transmission limits being maintained. The latter is modeled via (joint) chance constraints that are generally computationally intractable. Thus, we propose a solution approach based on the robust safe approximation of these constraints. Hereby, probabilistic constraints are replaced by robust constraints with suitably defined uncertainty sets constructed from real historical data. The uncertainty sets are calculated by encompassing randomly drawn scenarios using the scenario approach proposed by Margellos et al. (IEEE Transactions on Automatic Control, 59 (2014)). If the number of drawn samples is sufficiently large, it is possible to ensure that there is a high confidence probability that solutions of the robust approximation satisfy the optimization problem with joint chance constraints. The ability to discretely control the power feed-in then leads to a robust optimization problem with decision-dependent uncertainties, i.e. the uncertainty sets depend on decision variables. We propose an equivalent mixed-integer linear reformulation for box uncertainties. Due to the discrete decision-dependency of uncertainty sets, the reformulations contain bilinear terms for which we derive exact linearizations. Finally, we present numerical results for different test cases from the Nesta archive, as well as for a real network. We consider the discrete curtailment of solar feed-in, for which we use real-world weather and network data. The experimental tests demonstrate the effectiveness of this method and run times are very fast. Moreover, on average the calculated robust solutions lead to only to small increase in curtailment, when compared to nominal solutions. We conclude with an explanation of how this new methodology can be applied in practice.

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Metadaten
Author:Kevin-Martin Aigner, Jan-Patrick Clarner, Frauke Liers, Alexander Martin
Document Type:Preprint
Language:English
Date of Publication (online):2020/05/12
Date of first Publication:2020/05/12
Release Date:2020/05/12
Tag:chance constraints; decision-dependent uncertainty; optimal power flow; robust optimization; safe approximation
Institutes:Friedrich-Alexander-Universität Erlangen-Nürnberg
Zuse-Institut Berlin (ZIB)
Subprojects:A05
B06
B07
Z01
Licence (German):License LogoCreative Commons - CC BY - Namensnennung 4.0 International