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Ecient large-scale optimal power ow using convex relaxations and distributed optimization


A. Botros

Master Thesis, FS14 (10338)

The optimal power ow (OPF) problem is a fundamental decision problem in power systems and is central to secure and economic operation and control of power networks and markets. Since the 1960s, when the current problem formulation was rst stated, many di erent approaches have been made to solve it, including nonlinear programming, swarm optimization and interior point methods. Recently, the problem was reformulated as a semide nite program (SDP) using convex relaxation techniques, which allows a globally optimal solution to be computed in many practical cases. However, even though SDPs are convex and tractable in theory, it is still a challenge to solve these problems for large networks and multi period problems. In this thesis, two di erent methods are presented to solve large SDP relaxations of the optimal power ow problem faster and over multiple time steps. A sparse decomposition based on the matrix completion theorem substantially reduces the number of variables in the problem, exploiting the sparse structure of the transmission network and resulting in signi cant computational speed-ups. This is implemented in Python for general sparse SDP problems and applied to the OPF problem for single and multiperiod versions. Further, an ADMM-algorithm is developed to distribute the computation amongst multiple processors. These two methods and their associated bene ts can in principle be combined.


Type of Publication:

(12)Diploma/Master Thesis

T.H. Summers

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% Autogenerated BibTeX entry
@PhdThesis { Xxx:2014:IFA_5015
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