“20th PSCC 2018 papers submission and review platform

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Data-driven Security-Constrained AC-OPF for Operations and Markets

In this paper, we propose a data-driven preventive security-constrained AC optimal power flow (SC-OPF), which ensures small-signal stability and N-1 security. Our approach can be used by both system and market operators for optimizing redispatch or AC based market-clearing auctions. We derive decision trees from large datasets of operating points, which capture all security requirements and allow to define tractable decision rules that are implemented in the SC-OPF using mixed-integer nonlinear programming (MINLP). We propose a second-order cone relaxation for the non-convex MINLP, which allows us to translate the non-convex and possibly disjoint feasible space of secure system operation to a convex mixed-integer OPF formulation. Our case study shows that the proposed approach increases the feasible space represented in the SC-OPF compared to conventional methods, can identify the global optimum as opposed to tested MINLP solvers and significantly reduces computation time due to a decreased problem size.

Author(s):

Lejla Halilbasic    
Technical University of Denmark
Denmark

Florian Thams    
Technical University of Denmark
Denmark

Andreas Venzke    
Technical University of Denmark
Denmark

Spyros Chatzivasileiadis    
Technical University of Denmark
Denmark

Pierre Pinson    
Technical University of Denmark
Denmark

 

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